diff --git a/LICENSE b/LICENSE
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+# GLONET - Dual Licensing
+
+This project is distributed under a dual licensing scheme to separate the code from the learned parameters (weights).
+
+## 1. Source Code / Scripts
+
+The source code and scripts provided in this repository are released under the **European Union Public Licence (EUPL v1.2)**.
+
+Copyright (c) Mercator Ocean International
+
+Full license text: https://eupl.eu/1.2/en/
+
+## 2. Model Weights
+
+The model weights (parameters) stored in the `weights/` directory are released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.
+
+**You are free to:**
+- Share — copy and redistribute the material in any medium or format
+- Adapt — remix, transform, and build upon the material for any purpose, even commercially
+
+**Under the following terms:**
+- **Attribution** — You must give appropriate credit to **Mercator Ocean International**, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
+
+Full license text: https://creativecommons.org/licenses/by/4.0/
+
+## Disclaimer
+
+This model is for research and demonstration purposes. Mercator Ocean International makes no warranties regarding the accuracy or completeness of the outputs.
+
+## Contact
+
+For any information about GLONET, you can contact: glonet@mercator-ocean.eu
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--- a/xe_weights14/L763/thetao_mean.npy
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
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+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
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--- a/xe_weights14/L763/uo_mean.npy
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-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
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-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
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--- a/xe_weights14/L902/so_mean.npy
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
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-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
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diff --git a/xe_weights14/L902/uo_mean.npy b/xe_weights14/L902/uo_mean.npy
deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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+++ /dev/null
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diff --git a/xe_weights14/glonet_p1/.data/serialization_id b/xe_weights14/glonet_p1/.data/serialization_id
deleted file mode 100644
index 05717b60ac5c9b22284bb8a1c21e7cefb67244a8..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/.data/serialization_id
+++ /dev/null
@@ -1 +0,0 @@
-0371189840256180555107268580777949891317
\ No newline at end of file
diff --git a/xe_weights14/glonet_p1/.data/version b/xe_weights14/glonet_p1/.data/version
deleted file mode 100644
index f599e28b8ab0d8c9c57a486c89c4a5132dcbd3b2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/.data/version
+++ /dev/null
@@ -1 +0,0 @@
-10
diff --git a/xe_weights14/glonet_p1/byteorder b/xe_weights14/glonet_p1/byteorder
deleted file mode 100644
index a06f92f60ada3d71348371f5efc12c5ea640376a..0000000000000000000000000000000000000000
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+++ /dev/null
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-little
\ No newline at end of file
diff --git a/xe_weights14/glonet_p1/code/__torch__.py b/xe_weights14/glonet_p1/code/__torch__.py
deleted file mode 100644
index 089e8a2d8b7dca0fe14cee0883ec5be38426ecdc..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__.py
+++ /dev/null
@@ -1,29 +0,0 @@
-class SelectiveFilterLayer(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.SelectiveFilterLayer,
-    argument_1: Tensor) -> Tensor:
-    x_filtered = torch.clone(argument_1)
-    _0 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _1 = torch.slice(_0, 1, 0, 9223372036854775807)
-    _2 = torch.slice(torch.select(_1, 2, 1), 2, 0, 9223372036854775807)
-    _3 = torch.slice(_2, 3, 0, 9223372036854775807)
-    _4 = torch.clamp(_3, -1.45, 1.6299999999999999)
-    _5 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _6 = torch.slice(_5, 1, 0, 9223372036854775807)
-    _7 = torch.slice(torch.select(_6, 2, 1), 2, 0, 9223372036854775807)
-    _8 = torch.slice(_7, 3, 0, 9223372036854775807)
-    _9 = torch.copy_(_8, _4)
-    _10 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _11 = torch.slice(_10, 1, 0, 9223372036854775807)
-    _12 = torch.slice(torch.select(_11, 2, 2), 2, 0, 9223372036854775807)
-    _13 = torch.slice(_12, 3, 0, 9223372036854775807)
-    _14 = torch.clamp(_13, -14.7399144, 4.2854672000000003)
-    _15 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _16 = torch.slice(_15, 1, 0, 9223372036854775807)
-    _17 = torch.slice(torch.select(_16, 2, 2), 2, 0, 9223372036854775807)
-    _18 = torch.slice(_17, 3, 0, 9223372036854775807)
-    _19 = torch.copy_(_18, _14)
-    return x_filtered
diff --git a/xe_weights14/glonet_p1/code/__torch__.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8e70af428e88293d1ca24b6902e747d68e7cff0a1042c8ad5c9bc56667b65cda
-size 6847
diff --git a/xe_weights14/glonet_p1/code/__torch__/NN.py b/xe_weights14/glonet_p1/code/__torch__/NN.py
deleted file mode 100644
index 43ced0fc9300ec2722fc5a0360a8054be85f7711..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NN.py
+++ /dev/null
@@ -1,146 +0,0 @@
-class Local_CNN_Branch(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  upconv : __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d
-  def forward(self: __torch__.NN.Local_CNN_Branch,
-    x_raw: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x_raw, 0))
-    _0 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x_raw, 1))
-    _1 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x_raw, 2))
-    _2 = int(C)
-    _3 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x_raw, 3))
-    _4 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x_raw, 4))
-    input = torch.view(x_raw, [-1, _3, _4, int(W)])
-    _5 = (upconv).forward(input, )
-    _6 = ops.prim.NumToTensor(torch.size(_5, 2))
-    _7 = int(_6)
-    _8 = ops.prim.NumToTensor(torch.size(_5, 3))
-    lc_features = torch.view(_5, [_0, _1, _2, _7, int(_8)])
-    return lc_features
-  def forward1(self: __torch__.NN.Local_CNN_Branch,
-    x: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _9 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _10 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _11 = int(C)
-    _12 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _13 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    input = torch.view(x, [-1, _12, _13, int(W)])
-    _14 = (upconv).forward1(input, )
-    _15 = ops.prim.NumToTensor(torch.size(_14, 2))
-    _16 = int(_15)
-    _17 = ops.prim.NumToTensor(torch.size(_14, 3))
-    lc_features = torch.view(_14, [_9, _10, _11, _16, int(_17)])
-    return lc_features
-  def forward2(self: __torch__.NN.Local_CNN_Branch,
-    x: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _18 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _19 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _20 = int(C)
-    _21 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _22 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    input = torch.view(x, [-1, _21, _22, int(W)])
-    _23 = (upconv).forward2(input, )
-    _24 = ops.prim.NumToTensor(torch.size(_23, 2))
-    _25 = int(_24)
-    _26 = ops.prim.NumToTensor(torch.size(_23, 3))
-    lc_features = torch.view(_23, [_18, _19, _20, _25, int(_26)])
-    return lc_features
-  def forward3(self: __torch__.NN.Local_CNN_Branch,
-    x: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _27 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _28 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _29 = int(C)
-    _30 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _31 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    input = torch.view(x, [-1, _30, _31, int(W)])
-    _32 = (upconv).forward3(input, )
-    _33 = ops.prim.NumToTensor(torch.size(_32, 2))
-    _34 = int(_33)
-    _35 = ops.prim.NumToTensor(torch.size(_32, 3))
-    lc_features = torch.view(_32, [_27, _28, _29, _34, int(_35)])
-    return lc_features
-  def forward4(self: __torch__.NN.Local_CNN_Branch,
-    x: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _36 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _37 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _38 = int(C)
-    _39 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _40 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    input = torch.view(x, [-1, _39, _40, int(W)])
-    _41 = (upconv).forward4(input, )
-    _42 = ops.prim.NumToTensor(torch.size(_41, 2))
-    _43 = int(_42)
-    _44 = ops.prim.NumToTensor(torch.size(_41, 3))
-    lc_features = torch.view(_41, [_36, _37, _38, _43, int(_44)])
-    return lc_features
-  def forward5(self: __torch__.NN.Local_CNN_Branch,
-    x: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _45 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _46 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _47 = int(C)
-    _48 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _49 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    input = torch.view(x, [-1, _48, _49, int(W)])
-    _50 = (upconv).forward5(input, )
-    _51 = ops.prim.NumToTensor(torch.size(_50, 2))
-    _52 = int(_51)
-    _53 = ops.prim.NumToTensor(torch.size(_50, 3))
-    lc_features = torch.view(_50, [_45, _46, _47, _52, int(_53)])
-    return lc_features
-  def forward6(self: __torch__.NN.Local_CNN_Branch,
-    x: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _54 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _55 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _56 = int(C)
-    _57 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _58 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    input = torch.view(x, [-1, _57, _58, int(W)])
-    _59 = (upconv).forward6(input, )
-    _60 = ops.prim.NumToTensor(torch.size(_59, 2))
-    _61 = int(_60)
-    _62 = ops.prim.NumToTensor(torch.size(_59, 3))
-    lc_features = torch.view(_59, [_54, _55, _56, _61, int(_62)])
-    return lc_features
diff --git a/xe_weights14/glonet_p1/code/__torch__/NN.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NN.py.debug_pkl
deleted file mode 100644
index 444203f7b764b63c955d8895c0769cf89dc93ac5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NN.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c03a7112970640692c185591b47b11ec606bd4e702ad359a72f0e122f7b8bc40
-size 13186
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO.py b/xe_weights14/glonet_p1/code/__torch__/NO.py
deleted file mode 100644
index 99fd63184ee59af1e0ba8bbfb863efadc8cfadb4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO.py
+++ /dev/null
@@ -1,1099 +0,0 @@
-class GF_Block(Module):
-  __parameters__ = ["pos_embed", ]
-  __buffers__ = []
-  pos_embed : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  patch_embed : __torch__.NO.PatchEmbed
-  pos_drop : __torch__.torch.nn.modules.dropout.Dropout
-  blks : __torch__.torch.nn.modules.container.ModuleList
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_522.LayerNorm
-  lproj : __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential
-  final_dropout : __torch__.torch.nn.modules.linear.___torch_mangle_528.Identity
-  def forward(self: __torch__.NO.GF_Block,
-    x_raw: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks0 = self.blks
-    _10 = getattr(blks0, "10")
-    blks1 = self.blks
-    _9 = getattr(blks1, "9")
-    blks2 = self.blks
-    _8 = getattr(blks2, "8")
-    blks3 = self.blks
-    _7 = getattr(blks3, "7")
-    blks4 = self.blks
-    _6 = getattr(blks4, "6")
-    blks5 = self.blks
-    _5 = getattr(blks5, "5")
-    blks6 = self.blks
-    _4 = getattr(blks6, "4")
-    blks7 = self.blks
-    _3 = getattr(blks7, "3")
-    blks8 = self.blks
-    _2 = getattr(blks8, "2")
-    blks9 = self.blks
-    _1 = getattr(blks9, "1")
-    blks10 = self.blks
-    _0 = getattr(blks10, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x_raw, 0))
-    _12 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x_raw, 1))
-    _13 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x_raw, 2))
-    _14 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x_raw, 3))
-    _15 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x_raw, 4))
-    _16 = int(W)
-    B0 = ops.prim.NumToTensor(torch.size(x_raw, 0))
-    T0 = ops.prim.NumToTensor(torch.size(x_raw, 1))
-    C0 = ops.prim.NumToTensor(torch.size(x_raw, 2))
-    _17 = int(C0)
-    H0 = ops.prim.NumToTensor(torch.size(x_raw, 3))
-    _18 = int(H0)
-    W0 = ops.prim.NumToTensor(torch.size(x_raw, 4))
-    _19 = int(W0)
-    _20 = [int(torch.mul(B0, T0)), _17, _18, _19]
-    x = torch.view(x_raw, _20)
-    input = torch.add((patch_embed).forward(x, ), pos_embed)
-    _21 = (_0).forward((pos_drop).forward(input, ), )
-    _22 = (_3).forward((_2).forward((_1).forward(_21, ), ), )
-    _23 = (_6).forward((_5).forward((_4).forward(_22, ), ), )
-    _24 = (_9).forward((_8).forward((_7).forward(_23, ), ), )
-    _25 = (_11).forward((_10).forward(_24, ), )
-    _26 = torch.transpose((norm).forward(_25, ), 1, 2)
-    x0 = torch.contiguous(_26)
-    input0 = torch.reshape(x0, [-1, 768, 42, 90])
-    _27 = (final_dropout).forward()
-    gf_features = torch.reshape((lproj).forward(input0, ), [_12, _13, _14, _15, _16])
-    return gf_features
-  def forward1(self: __torch__.NO.GF_Block,
-    x: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks11 = self.blks
-    _10 = getattr(blks11, "10")
-    blks12 = self.blks
-    _9 = getattr(blks12, "9")
-    blks13 = self.blks
-    _8 = getattr(blks13, "8")
-    blks14 = self.blks
-    _7 = getattr(blks14, "7")
-    blks15 = self.blks
-    _6 = getattr(blks15, "6")
-    blks16 = self.blks
-    _5 = getattr(blks16, "5")
-    blks17 = self.blks
-    _4 = getattr(blks17, "4")
-    blks18 = self.blks
-    _3 = getattr(blks18, "3")
-    blks19 = self.blks
-    _2 = getattr(blks19, "2")
-    blks20 = self.blks
-    _1 = getattr(blks20, "1")
-    blks21 = self.blks
-    _0 = getattr(blks21, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _16 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _17 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _18 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _19 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _20 = int(W)
-    B1 = ops.prim.NumToTensor(torch.size(x, 0))
-    T1 = ops.prim.NumToTensor(torch.size(x, 1))
-    C1 = ops.prim.NumToTensor(torch.size(x, 2))
-    _21 = int(C1)
-    H1 = ops.prim.NumToTensor(torch.size(x, 3))
-    _22 = int(H1)
-    W1 = ops.prim.NumToTensor(torch.size(x, 4))
-    _23 = int(W1)
-    _24 = [int(torch.mul(B1, T1)), _21, _22, _23]
-    x1 = torch.view(x, _24)
-    input = torch.add((patch_embed).forward1(x1, ), pos_embed)
-    _25 = (_0).forward1((pos_drop).forward1(input, ), )
-    _26 = (_2).forward1((_1).forward1(_25, ), )
-    _27 = (_4).forward1((_3).forward1(_26, ), )
-    _28 = (_6).forward1((_5).forward1(_27, ), )
-    _29 = (_8).forward1((_7).forward1(_28, ), )
-    _30 = (_10).forward1((_9).forward1(_29, ), )
-    _31 = (norm).forward1((_11).forward1(_30, ), )
-    x2 = torch.contiguous(torch.transpose(_31, 1, 2))
-    input1 = torch.reshape(x2, [-1, 768, 42, 90])
-    _32 = (final_dropout).forward1()
-    gf_features = torch.reshape((lproj).forward1(input1, ), [_16, _17, _18, _19, _20])
-    return gf_features
-  def forward2(self: __torch__.NO.GF_Block,
-    x: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks22 = self.blks
-    _10 = getattr(blks22, "10")
-    blks23 = self.blks
-    _9 = getattr(blks23, "9")
-    blks24 = self.blks
-    _8 = getattr(blks24, "8")
-    blks25 = self.blks
-    _7 = getattr(blks25, "7")
-    blks26 = self.blks
-    _6 = getattr(blks26, "6")
-    blks27 = self.blks
-    _5 = getattr(blks27, "5")
-    blks28 = self.blks
-    _4 = getattr(blks28, "4")
-    blks29 = self.blks
-    _3 = getattr(blks29, "3")
-    blks30 = self.blks
-    _2 = getattr(blks30, "2")
-    blks31 = self.blks
-    _1 = getattr(blks31, "1")
-    blks32 = self.blks
-    _0 = getattr(blks32, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _33 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _34 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _35 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _36 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _37 = int(W)
-    B2 = ops.prim.NumToTensor(torch.size(x, 0))
-    T2 = ops.prim.NumToTensor(torch.size(x, 1))
-    C2 = ops.prim.NumToTensor(torch.size(x, 2))
-    _38 = int(C2)
-    H2 = ops.prim.NumToTensor(torch.size(x, 3))
-    _39 = int(H2)
-    W2 = ops.prim.NumToTensor(torch.size(x, 4))
-    _40 = int(W2)
-    _41 = [int(torch.mul(B2, T2)), _38, _39, _40]
-    x3 = torch.view(x, _41)
-    input = torch.add((patch_embed).forward2(x3, ), pos_embed)
-    _42 = (_0).forward2((pos_drop).forward2(input, ), )
-    _43 = (_2).forward2((_1).forward2(_42, ), )
-    _44 = (_4).forward2((_3).forward2(_43, ), )
-    _45 = (_6).forward2((_5).forward2(_44, ), )
-    _46 = (_8).forward2((_7).forward2(_45, ), )
-    _47 = (_10).forward2((_9).forward2(_46, ), )
-    _48 = (norm).forward2((_11).forward2(_47, ), )
-    x4 = torch.contiguous(torch.transpose(_48, 1, 2))
-    input2 = torch.reshape(x4, [-1, 768, 42, 90])
-    _49 = (final_dropout).forward2()
-    gf_features = torch.reshape((lproj).forward2(input2, ), [_33, _34, _35, _36, _37])
-    return gf_features
-  def forward3(self: __torch__.NO.GF_Block,
-    x: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks33 = self.blks
-    _10 = getattr(blks33, "10")
-    blks34 = self.blks
-    _9 = getattr(blks34, "9")
-    blks35 = self.blks
-    _8 = getattr(blks35, "8")
-    blks36 = self.blks
-    _7 = getattr(blks36, "7")
-    blks37 = self.blks
-    _6 = getattr(blks37, "6")
-    blks38 = self.blks
-    _5 = getattr(blks38, "5")
-    blks39 = self.blks
-    _4 = getattr(blks39, "4")
-    blks40 = self.blks
-    _3 = getattr(blks40, "3")
-    blks41 = self.blks
-    _2 = getattr(blks41, "2")
-    blks42 = self.blks
-    _1 = getattr(blks42, "1")
-    blks43 = self.blks
-    _0 = getattr(blks43, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _50 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _51 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _52 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _53 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _54 = int(W)
-    B3 = ops.prim.NumToTensor(torch.size(x, 0))
-    T3 = ops.prim.NumToTensor(torch.size(x, 1))
-    C3 = ops.prim.NumToTensor(torch.size(x, 2))
-    _55 = int(C3)
-    H3 = ops.prim.NumToTensor(torch.size(x, 3))
-    _56 = int(H3)
-    W3 = ops.prim.NumToTensor(torch.size(x, 4))
-    _57 = int(W3)
-    _58 = [int(torch.mul(B3, T3)), _55, _56, _57]
-    x5 = torch.view(x, _58)
-    input = torch.add((patch_embed).forward3(x5, ), pos_embed)
-    _59 = (_0).forward3((pos_drop).forward3(input, ), )
-    _60 = (_2).forward3((_1).forward3(_59, ), )
-    _61 = (_4).forward3((_3).forward3(_60, ), )
-    _62 = (_6).forward3((_5).forward3(_61, ), )
-    _63 = (_8).forward3((_7).forward3(_62, ), )
-    _64 = (_10).forward3((_9).forward3(_63, ), )
-    _65 = (norm).forward3((_11).forward3(_64, ), )
-    x6 = torch.contiguous(torch.transpose(_65, 1, 2))
-    input3 = torch.reshape(x6, [-1, 768, 42, 90])
-    _66 = (final_dropout).forward3()
-    gf_features = torch.reshape((lproj).forward3(input3, ), [_50, _51, _52, _53, _54])
-    return gf_features
-  def forward4(self: __torch__.NO.GF_Block,
-    x: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks44 = self.blks
-    _10 = getattr(blks44, "10")
-    blks45 = self.blks
-    _9 = getattr(blks45, "9")
-    blks46 = self.blks
-    _8 = getattr(blks46, "8")
-    blks47 = self.blks
-    _7 = getattr(blks47, "7")
-    blks48 = self.blks
-    _6 = getattr(blks48, "6")
-    blks49 = self.blks
-    _5 = getattr(blks49, "5")
-    blks50 = self.blks
-    _4 = getattr(blks50, "4")
-    blks51 = self.blks
-    _3 = getattr(blks51, "3")
-    blks52 = self.blks
-    _2 = getattr(blks52, "2")
-    blks53 = self.blks
-    _1 = getattr(blks53, "1")
-    blks54 = self.blks
-    _0 = getattr(blks54, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _67 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _68 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _69 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _70 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _71 = int(W)
-    B4 = ops.prim.NumToTensor(torch.size(x, 0))
-    T4 = ops.prim.NumToTensor(torch.size(x, 1))
-    C4 = ops.prim.NumToTensor(torch.size(x, 2))
-    _72 = int(C4)
-    H4 = ops.prim.NumToTensor(torch.size(x, 3))
-    _73 = int(H4)
-    W4 = ops.prim.NumToTensor(torch.size(x, 4))
-    _74 = int(W4)
-    _75 = [int(torch.mul(B4, T4)), _72, _73, _74]
-    x7 = torch.view(x, _75)
-    input = torch.add((patch_embed).forward4(x7, ), pos_embed)
-    _76 = (_0).forward4((pos_drop).forward4(input, ), )
-    _77 = (_2).forward4((_1).forward4(_76, ), )
-    _78 = (_4).forward4((_3).forward4(_77, ), )
-    _79 = (_6).forward4((_5).forward4(_78, ), )
-    _80 = (_8).forward4((_7).forward4(_79, ), )
-    _81 = (_10).forward4((_9).forward4(_80, ), )
-    _82 = (norm).forward4((_11).forward4(_81, ), )
-    x8 = torch.contiguous(torch.transpose(_82, 1, 2))
-    input4 = torch.reshape(x8, [-1, 768, 42, 90])
-    _83 = (final_dropout).forward4()
-    gf_features = torch.reshape((lproj).forward4(input4, ), [_67, _68, _69, _70, _71])
-    return gf_features
-  def forward5(self: __torch__.NO.GF_Block,
-    x: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks55 = self.blks
-    _10 = getattr(blks55, "10")
-    blks56 = self.blks
-    _9 = getattr(blks56, "9")
-    blks57 = self.blks
-    _8 = getattr(blks57, "8")
-    blks58 = self.blks
-    _7 = getattr(blks58, "7")
-    blks59 = self.blks
-    _6 = getattr(blks59, "6")
-    blks60 = self.blks
-    _5 = getattr(blks60, "5")
-    blks61 = self.blks
-    _4 = getattr(blks61, "4")
-    blks62 = self.blks
-    _3 = getattr(blks62, "3")
-    blks63 = self.blks
-    _2 = getattr(blks63, "2")
-    blks64 = self.blks
-    _1 = getattr(blks64, "1")
-    blks65 = self.blks
-    _0 = getattr(blks65, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _84 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _85 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _86 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _87 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _88 = int(W)
-    B5 = ops.prim.NumToTensor(torch.size(x, 0))
-    T5 = ops.prim.NumToTensor(torch.size(x, 1))
-    C5 = ops.prim.NumToTensor(torch.size(x, 2))
-    _89 = int(C5)
-    H5 = ops.prim.NumToTensor(torch.size(x, 3))
-    _90 = int(H5)
-    W5 = ops.prim.NumToTensor(torch.size(x, 4))
-    _91 = int(W5)
-    _92 = [int(torch.mul(B5, T5)), _89, _90, _91]
-    x9 = torch.view(x, _92)
-    input = torch.add((patch_embed).forward5(x9, ), pos_embed)
-    _93 = (_0).forward5((pos_drop).forward5(input, ), )
-    _94 = (_2).forward5((_1).forward5(_93, ), )
-    _95 = (_4).forward5((_3).forward5(_94, ), )
-    _96 = (_6).forward5((_5).forward5(_95, ), )
-    _97 = (_8).forward5((_7).forward5(_96, ), )
-    _98 = (_10).forward5((_9).forward5(_97, ), )
-    _99 = (norm).forward5((_11).forward5(_98, ), )
-    x10 = torch.contiguous(torch.transpose(_99, 1, 2))
-    input5 = torch.reshape(x10, [-1, 768, 42, 90])
-    _100 = (final_dropout).forward5()
-    gf_features = torch.reshape((lproj).forward5(input5, ), [_84, _85, _86, _87, _88])
-    return gf_features
-  def forward6(self: __torch__.NO.GF_Block,
-    x: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks66 = self.blks
-    _10 = getattr(blks66, "10")
-    blks67 = self.blks
-    _9 = getattr(blks67, "9")
-    blks68 = self.blks
-    _8 = getattr(blks68, "8")
-    blks69 = self.blks
-    _7 = getattr(blks69, "7")
-    blks70 = self.blks
-    _6 = getattr(blks70, "6")
-    blks71 = self.blks
-    _5 = getattr(blks71, "5")
-    blks72 = self.blks
-    _4 = getattr(blks72, "4")
-    blks73 = self.blks
-    _3 = getattr(blks73, "3")
-    blks74 = self.blks
-    _2 = getattr(blks74, "2")
-    blks75 = self.blks
-    _1 = getattr(blks75, "1")
-    blks76 = self.blks
-    _0 = getattr(blks76, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _101 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _102 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _103 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _104 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _105 = int(W)
-    B6 = ops.prim.NumToTensor(torch.size(x, 0))
-    T6 = ops.prim.NumToTensor(torch.size(x, 1))
-    C6 = ops.prim.NumToTensor(torch.size(x, 2))
-    _106 = int(C6)
-    H6 = ops.prim.NumToTensor(torch.size(x, 3))
-    _107 = int(H6)
-    W6 = ops.prim.NumToTensor(torch.size(x, 4))
-    _108 = int(W6)
-    _109 = [int(torch.mul(B6, T6)), _106, _107, _108]
-    x11 = torch.view(x, _109)
-    input = torch.add((patch_embed).forward6(x11, ), pos_embed)
-    _110 = (_0).forward6((pos_drop).forward6(input, ), )
-    _111 = (_2).forward6((_1).forward6(_110, ), )
-    _112 = (_4).forward6((_3).forward6(_111, ), )
-    _113 = (_6).forward6((_5).forward6(_112, ), )
-    _114 = (_8).forward6((_7).forward6(_113, ), )
-    _115 = (_10).forward6((_9).forward6(_114, ), )
-    _116 = (norm).forward6((_11).forward6(_115, ), )
-    x12 = torch.contiguous(torch.transpose(_116, 1, 2))
-    input6 = torch.reshape(x12, [-1, 768, 42, 90])
-    _117 = (final_dropout).forward6()
-    gf_features = torch.reshape((lproj).forward6(input6, ), [_101, _102, _103, _104, _105])
-    return gf_features
-class PatchEmbed(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  projection : __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d
-  norm : __torch__.torch.nn.modules.linear.Identity
-  def forward(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _118 = torch.flatten((projection).forward(x, ), 2)
-    x13 = torch.contiguous(torch.transpose(_118, 1, 2))
-    _119 = (norm).forward()
-    return x13
-  def forward1(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _120 = torch.flatten((projection).forward1(x, ), 2)
-    x14 = torch.contiguous(torch.transpose(_120, 1, 2))
-    _121 = (norm).forward1()
-    return x14
-  def forward2(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _122 = torch.flatten((projection).forward2(x, ), 2)
-    x15 = torch.contiguous(torch.transpose(_122, 1, 2))
-    _123 = (norm).forward2()
-    return x15
-  def forward3(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _124 = torch.flatten((projection).forward3(x, ), 2)
-    x16 = torch.contiguous(torch.transpose(_124, 1, 2))
-    _125 = (norm).forward3()
-    return x16
-  def forward4(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _126 = torch.flatten((projection).forward4(x, ), 2)
-    x17 = torch.contiguous(torch.transpose(_126, 1, 2))
-    _127 = (norm).forward4()
-    return x17
-  def forward5(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _128 = torch.flatten((projection).forward5(x, ), 2)
-    x18 = torch.contiguous(torch.transpose(_128, 1, 2))
-    _129 = (norm).forward5()
-    return x18
-  def forward6(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _130 = torch.flatten((projection).forward6(x, ), 2)
-    x19 = torch.contiguous(torch.transpose(_130, 1, 2))
-    _131 = (norm).forward6()
-    return x19
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.LayerNorm
-  filter : __torch__.NO.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_342.Identity
-  attn : __torch__.NO.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_343.LayerNorm
-  mlp : __torch__.NO.Mlp
-  def forward(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _132 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _133 = (drop_path).forward()
-    input = torch.add(argument_1, _132)
-    _134 = (mlp).forward((normlayer2).forward(input, ), )
-    _135 = (drop_path).forward1()
-    return torch.add(input, _134)
-  def forward1(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _136 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _137 = (drop_path).forward2()
-    input = torch.add(argument_1, _136)
-    _138 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _139 = (drop_path).forward3()
-    return torch.add(input, _138)
-  def forward2(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _140 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _141 = (drop_path).forward4()
-    input = torch.add(argument_1, _140)
-    _142 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _143 = (drop_path).forward5()
-    return torch.add(input, _142)
-  def forward3(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _144 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _145 = (drop_path).forward6()
-    input = torch.add(argument_1, _144)
-    _146 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _147 = (drop_path).forward7()
-    return torch.add(input, _146)
-  def forward4(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _148 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _149 = (drop_path).forward8()
-    input = torch.add(argument_1, _148)
-    _150 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _151 = (drop_path).forward9()
-    return torch.add(input, _150)
-  def forward5(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _152 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _153 = (drop_path).forward10()
-    input = torch.add(argument_1, _152)
-    _154 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _155 = (drop_path).forward11()
-    return torch.add(input, _154)
-  def forward6(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _156 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _157 = (drop_path).forward12()
-    input = torch.add(argument_1, _156)
-    _158 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _159 = (drop_path).forward13()
-    return torch.add(input, _158)
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.ReLU
-  bias : __torch__.torch.nn.modules.conv.Conv1d
-  def forward(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _160 = int(B)
-    _161 = int(B)
-    _162 = int(B)
-    _163 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _164 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _165 = int(C)
-    _166 = int(C)
-    _167 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_167)
-    _168 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_168)
-    x = torch.reshape(argument_1, [_163, 42, 90, _166])
-    x20 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _169 = ops.prim.NumToTensor(torch.size(x20, 1))
-    _170 = int(_169)
-    _171 = ops.prim.NumToTensor(torch.size(x20, 2))
-    x21 = torch.reshape(x20, [_162, _170, int(_171), 2, 384])
-    _172 = [torch.real(x21), torch.select(w1, 0, 0)]
-    _173 = torch.einsum("...bd, bdk->...bk", _172)
-    _174 = [torch.imag(x21), torch.select(w1, 0, 1)]
-    _175 = torch.einsum("...bd, bdk->...bk", _174)
-    input7 = torch.add(torch.sub(_173, _175), torch.select(b1, 0, 0))
-    _176 = torch.relu_(input7)
-    _177 = [torch.real(x21), torch.select(w1, 0, 1)]
-    _178 = torch.einsum("...bd, bdk->...bk", _177)
-    _179 = [torch.imag(x21), torch.select(w1, 0, 0)]
-    _180 = torch.einsum("...bd, bdk->...bk", _179)
-    input8 = torch.add(torch.add(_178, _180), torch.select(b1, 0, 1))
-    _181 = torch.relu_(input8)
-    _182 = torch.einsum("...bd, bdk->...bk", [_176, torch.select(w2, 0, 0)])
-    _183 = torch.einsum("...bd, bdk->...bk", [_181, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_182, _183), torch.select(b2, 0, 0))
-    _184 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _185 = torch.einsum("...bd, bdk->...bk", [_181, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_184, _185), torch.select(b2, 0, 1))
-    x22 = torch.stack([x_real, x_imag], -1)
-    x23 = torch.view_as_complex(x22)
-    _186 = ops.prim.NumToTensor(torch.size(x23, 1))
-    _187 = int(_186)
-    _188 = ops.prim.NumToTensor(torch.size(x23, 2))
-    x24 = torch.reshape(x23, [_161, _187, int(_188), 768])
-    x25 = torch.fft_irfft2(x24, [42, 90], [1, 2], "ortho")
-    x26 = torch.reshape(x25, [_160, _164, _165])
-    return torch.add(x26, bias0)
-  def forward1(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _189 = int(B)
-    _190 = int(B)
-    _191 = int(B)
-    _192 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _193 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _194 = int(C)
-    _195 = int(C)
-    _196 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_196)
-    _197 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_197)
-    x = torch.reshape(argument_1, [_192, 42, 90, _195])
-    x27 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _198 = ops.prim.NumToTensor(torch.size(x27, 1))
-    _199 = int(_198)
-    _200 = ops.prim.NumToTensor(torch.size(x27, 2))
-    x28 = torch.reshape(x27, [_191, _199, int(_200), 2, 384])
-    _201 = [torch.real(x28), torch.select(w1, 0, 0)]
-    _202 = torch.einsum("...bd, bdk->...bk", _201)
-    _203 = [torch.imag(x28), torch.select(w1, 0, 1)]
-    _204 = torch.einsum("...bd, bdk->...bk", _203)
-    input9 = torch.add(torch.sub(_202, _204), torch.select(b1, 0, 0))
-    _205 = torch.relu_(input9)
-    _206 = [torch.real(x28), torch.select(w1, 0, 1)]
-    _207 = torch.einsum("...bd, bdk->...bk", _206)
-    _208 = [torch.imag(x28), torch.select(w1, 0, 0)]
-    _209 = torch.einsum("...bd, bdk->...bk", _208)
-    input10 = torch.add(torch.add(_207, _209), torch.select(b1, 0, 1))
-    _210 = torch.relu_(input10)
-    _211 = torch.einsum("...bd, bdk->...bk", [_205, torch.select(w2, 0, 0)])
-    _212 = torch.einsum("...bd, bdk->...bk", [_210, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_211, _212), torch.select(b2, 0, 0))
-    _213 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _214 = torch.einsum("...bd, bdk->...bk", [_210, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_213, _214), torch.select(b2, 0, 1))
-    x29 = torch.stack([x_real, x_imag], -1)
-    x30 = torch.view_as_complex(x29)
-    _215 = ops.prim.NumToTensor(torch.size(x30, 1))
-    _216 = int(_215)
-    _217 = ops.prim.NumToTensor(torch.size(x30, 2))
-    x31 = torch.reshape(x30, [_190, _216, int(_217), 768])
-    x32 = torch.fft_irfft2(x31, [42, 90], [1, 2], "ortho")
-    x33 = torch.reshape(x32, [_189, _193, _194])
-    return torch.add(x33, bias1)
-  def forward2(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _218 = int(B)
-    _219 = int(B)
-    _220 = int(B)
-    _221 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _222 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _223 = int(C)
-    _224 = int(C)
-    _225 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_225)
-    _226 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_226)
-    x = torch.reshape(argument_1, [_221, 42, 90, _224])
-    x34 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _227 = ops.prim.NumToTensor(torch.size(x34, 1))
-    _228 = int(_227)
-    _229 = ops.prim.NumToTensor(torch.size(x34, 2))
-    x35 = torch.reshape(x34, [_220, _228, int(_229), 2, 384])
-    _230 = [torch.real(x35), torch.select(w1, 0, 0)]
-    _231 = torch.einsum("...bd, bdk->...bk", _230)
-    _232 = [torch.imag(x35), torch.select(w1, 0, 1)]
-    _233 = torch.einsum("...bd, bdk->...bk", _232)
-    input11 = torch.add(torch.sub(_231, _233), torch.select(b1, 0, 0))
-    _234 = torch.relu_(input11)
-    _235 = [torch.real(x35), torch.select(w1, 0, 1)]
-    _236 = torch.einsum("...bd, bdk->...bk", _235)
-    _237 = [torch.imag(x35), torch.select(w1, 0, 0)]
-    _238 = torch.einsum("...bd, bdk->...bk", _237)
-    input12 = torch.add(torch.add(_236, _238), torch.select(b1, 0, 1))
-    _239 = torch.relu_(input12)
-    _240 = torch.einsum("...bd, bdk->...bk", [_234, torch.select(w2, 0, 0)])
-    _241 = torch.einsum("...bd, bdk->...bk", [_239, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_240, _241), torch.select(b2, 0, 0))
-    _242 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _243 = torch.einsum("...bd, bdk->...bk", [_239, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_242, _243), torch.select(b2, 0, 1))
-    x36 = torch.stack([x_real, x_imag], -1)
-    x37 = torch.view_as_complex(x36)
-    _244 = ops.prim.NumToTensor(torch.size(x37, 1))
-    _245 = int(_244)
-    _246 = ops.prim.NumToTensor(torch.size(x37, 2))
-    x38 = torch.reshape(x37, [_219, _245, int(_246), 768])
-    x39 = torch.fft_irfft2(x38, [42, 90], [1, 2], "ortho")
-    x40 = torch.reshape(x39, [_218, _222, _223])
-    return torch.add(x40, bias2)
-  def forward3(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _247 = int(B)
-    _248 = int(B)
-    _249 = int(B)
-    _250 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _251 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _252 = int(C)
-    _253 = int(C)
-    _254 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_254)
-    _255 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_255)
-    x = torch.reshape(argument_1, [_250, 42, 90, _253])
-    x41 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _256 = ops.prim.NumToTensor(torch.size(x41, 1))
-    _257 = int(_256)
-    _258 = ops.prim.NumToTensor(torch.size(x41, 2))
-    x42 = torch.reshape(x41, [_249, _257, int(_258), 2, 384])
-    _259 = [torch.real(x42), torch.select(w1, 0, 0)]
-    _260 = torch.einsum("...bd, bdk->...bk", _259)
-    _261 = [torch.imag(x42), torch.select(w1, 0, 1)]
-    _262 = torch.einsum("...bd, bdk->...bk", _261)
-    input13 = torch.add(torch.sub(_260, _262), torch.select(b1, 0, 0))
-    _263 = torch.relu_(input13)
-    _264 = [torch.real(x42), torch.select(w1, 0, 1)]
-    _265 = torch.einsum("...bd, bdk->...bk", _264)
-    _266 = [torch.imag(x42), torch.select(w1, 0, 0)]
-    _267 = torch.einsum("...bd, bdk->...bk", _266)
-    input14 = torch.add(torch.add(_265, _267), torch.select(b1, 0, 1))
-    _268 = torch.relu_(input14)
-    _269 = torch.einsum("...bd, bdk->...bk", [_263, torch.select(w2, 0, 0)])
-    _270 = torch.einsum("...bd, bdk->...bk", [_268, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_269, _270), torch.select(b2, 0, 0))
-    _271 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _272 = torch.einsum("...bd, bdk->...bk", [_268, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_271, _272), torch.select(b2, 0, 1))
-    x43 = torch.stack([x_real, x_imag], -1)
-    x44 = torch.view_as_complex(x43)
-    _273 = ops.prim.NumToTensor(torch.size(x44, 1))
-    _274 = int(_273)
-    _275 = ops.prim.NumToTensor(torch.size(x44, 2))
-    x45 = torch.reshape(x44, [_248, _274, int(_275), 768])
-    x46 = torch.fft_irfft2(x45, [42, 90], [1, 2], "ortho")
-    x47 = torch.reshape(x46, [_247, _251, _252])
-    return torch.add(x47, bias3)
-  def forward4(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _276 = int(B)
-    _277 = int(B)
-    _278 = int(B)
-    _279 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _280 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _281 = int(C)
-    _282 = int(C)
-    _283 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_283)
-    _284 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_284)
-    x = torch.reshape(argument_1, [_279, 42, 90, _282])
-    x48 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _285 = ops.prim.NumToTensor(torch.size(x48, 1))
-    _286 = int(_285)
-    _287 = ops.prim.NumToTensor(torch.size(x48, 2))
-    x49 = torch.reshape(x48, [_278, _286, int(_287), 2, 384])
-    _288 = [torch.real(x49), torch.select(w1, 0, 0)]
-    _289 = torch.einsum("...bd, bdk->...bk", _288)
-    _290 = [torch.imag(x49), torch.select(w1, 0, 1)]
-    _291 = torch.einsum("...bd, bdk->...bk", _290)
-    input15 = torch.add(torch.sub(_289, _291), torch.select(b1, 0, 0))
-    _292 = torch.relu_(input15)
-    _293 = [torch.real(x49), torch.select(w1, 0, 1)]
-    _294 = torch.einsum("...bd, bdk->...bk", _293)
-    _295 = [torch.imag(x49), torch.select(w1, 0, 0)]
-    _296 = torch.einsum("...bd, bdk->...bk", _295)
-    input16 = torch.add(torch.add(_294, _296), torch.select(b1, 0, 1))
-    _297 = torch.relu_(input16)
-    _298 = torch.einsum("...bd, bdk->...bk", [_292, torch.select(w2, 0, 0)])
-    _299 = torch.einsum("...bd, bdk->...bk", [_297, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_298, _299), torch.select(b2, 0, 0))
-    _300 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _301 = torch.einsum("...bd, bdk->...bk", [_297, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_300, _301), torch.select(b2, 0, 1))
-    x50 = torch.stack([x_real, x_imag], -1)
-    x51 = torch.view_as_complex(x50)
-    _302 = ops.prim.NumToTensor(torch.size(x51, 1))
-    _303 = int(_302)
-    _304 = ops.prim.NumToTensor(torch.size(x51, 2))
-    x52 = torch.reshape(x51, [_277, _303, int(_304), 768])
-    x53 = torch.fft_irfft2(x52, [42, 90], [1, 2], "ortho")
-    x54 = torch.reshape(x53, [_276, _280, _281])
-    return torch.add(x54, bias4)
-  def forward5(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _305 = int(B)
-    _306 = int(B)
-    _307 = int(B)
-    _308 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _309 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _310 = int(C)
-    _311 = int(C)
-    _312 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_312)
-    _313 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_313)
-    x = torch.reshape(argument_1, [_308, 42, 90, _311])
-    x55 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _314 = ops.prim.NumToTensor(torch.size(x55, 1))
-    _315 = int(_314)
-    _316 = ops.prim.NumToTensor(torch.size(x55, 2))
-    x56 = torch.reshape(x55, [_307, _315, int(_316), 2, 384])
-    _317 = [torch.real(x56), torch.select(w1, 0, 0)]
-    _318 = torch.einsum("...bd, bdk->...bk", _317)
-    _319 = [torch.imag(x56), torch.select(w1, 0, 1)]
-    _320 = torch.einsum("...bd, bdk->...bk", _319)
-    input17 = torch.add(torch.sub(_318, _320), torch.select(b1, 0, 0))
-    _321 = torch.relu_(input17)
-    _322 = [torch.real(x56), torch.select(w1, 0, 1)]
-    _323 = torch.einsum("...bd, bdk->...bk", _322)
-    _324 = [torch.imag(x56), torch.select(w1, 0, 0)]
-    _325 = torch.einsum("...bd, bdk->...bk", _324)
-    input18 = torch.add(torch.add(_323, _325), torch.select(b1, 0, 1))
-    _326 = torch.relu_(input18)
-    _327 = torch.einsum("...bd, bdk->...bk", [_321, torch.select(w2, 0, 0)])
-    _328 = torch.einsum("...bd, bdk->...bk", [_326, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_327, _328), torch.select(b2, 0, 0))
-    _329 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _330 = torch.einsum("...bd, bdk->...bk", [_326, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_329, _330), torch.select(b2, 0, 1))
-    x57 = torch.stack([x_real, x_imag], -1)
-    x58 = torch.view_as_complex(x57)
-    _331 = ops.prim.NumToTensor(torch.size(x58, 1))
-    _332 = int(_331)
-    _333 = ops.prim.NumToTensor(torch.size(x58, 2))
-    x59 = torch.reshape(x58, [_306, _332, int(_333), 768])
-    x60 = torch.fft_irfft2(x59, [42, 90], [1, 2], "ortho")
-    x61 = torch.reshape(x60, [_305, _309, _310])
-    return torch.add(x61, bias5)
-  def forward6(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _334 = int(B)
-    _335 = int(B)
-    _336 = int(B)
-    _337 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _338 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _339 = int(C)
-    _340 = int(C)
-    _341 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_341)
-    _342 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_342)
-    x = torch.reshape(argument_1, [_337, 42, 90, _340])
-    x62 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _343 = ops.prim.NumToTensor(torch.size(x62, 1))
-    _344 = int(_343)
-    _345 = ops.prim.NumToTensor(torch.size(x62, 2))
-    x63 = torch.reshape(x62, [_336, _344, int(_345), 2, 384])
-    _346 = [torch.real(x63), torch.select(w1, 0, 0)]
-    _347 = torch.einsum("...bd, bdk->...bk", _346)
-    _348 = [torch.imag(x63), torch.select(w1, 0, 1)]
-    _349 = torch.einsum("...bd, bdk->...bk", _348)
-    input19 = torch.add(torch.sub(_347, _349), torch.select(b1, 0, 0))
-    _350 = torch.relu_(input19)
-    _351 = [torch.real(x63), torch.select(w1, 0, 1)]
-    _352 = torch.einsum("...bd, bdk->...bk", _351)
-    _353 = [torch.imag(x63), torch.select(w1, 0, 0)]
-    _354 = torch.einsum("...bd, bdk->...bk", _353)
-    input20 = torch.add(torch.add(_352, _354), torch.select(b1, 0, 1))
-    _355 = torch.relu_(input20)
-    _356 = torch.einsum("...bd, bdk->...bk", [_350, torch.select(w2, 0, 0)])
-    _357 = torch.einsum("...bd, bdk->...bk", [_355, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_356, _357), torch.select(b2, 0, 0))
-    _358 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _359 = torch.einsum("...bd, bdk->...bk", [_355, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_358, _359), torch.select(b2, 0, 1))
-    x64 = torch.stack([x_real, x_imag], -1)
-    x65 = torch.view_as_complex(x64)
-    _360 = ops.prim.NumToTensor(torch.size(x65, 1))
-    _361 = int(_360)
-    _362 = ops.prim.NumToTensor(torch.size(x65, 2))
-    x66 = torch.reshape(x65, [_335, _361, int(_362), 768])
-    x67 = torch.fft_irfft2(x66, [42, 90], [1, 2], "ortho")
-    x68 = torch.reshape(x67, [_334, _338, _339])
-    return torch.add(x68, bias6)
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.MultiheadAttention
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.Linear
-  act : __torch__.torch.nn.modules.activation.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_344.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout
-  def forward(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _363 = (act).forward((fc1).forward(argument_1, ), )
-    _364 = (fc3).forward((drop).forward(_363, ), )
-    return (drop).forward1(_364, )
-  def forward1(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _365 = (act).forward1((fc1).forward1(argument_1, ), )
-    _366 = (fc3).forward1((drop).forward2(_365, ), )
-    return (drop).forward3(_366, )
-  def forward2(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _367 = (act).forward2((fc1).forward2(argument_1, ), )
-    _368 = (fc3).forward2((drop).forward4(_367, ), )
-    return (drop).forward5(_368, )
-  def forward3(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _369 = (act).forward3((fc1).forward3(argument_1, ), )
-    _370 = (fc3).forward3((drop).forward6(_369, ), )
-    return (drop).forward7(_370, )
-  def forward4(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _371 = (act).forward4((fc1).forward4(argument_1, ), )
-    _372 = (fc3).forward4((drop).forward8(_371, ), )
-    return (drop).forward9(_372, )
-  def forward5(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _373 = (act).forward5((fc1).forward5(argument_1, ), )
-    _374 = (fc3).forward5((drop).forward10(_373, ), )
-    return (drop).forward11(_374, )
-  def forward6(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _375 = (act).forward6((fc1).forward6(argument_1, ), )
-    _376 = (fc3).forward6((drop).forward12(_375, ), )
-    return (drop).forward13(_376, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO.py.debug_pkl
deleted file mode 100644
index 56ff1fd9c052bc802f2f73458e20a8f2b71be3bd..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6e44bc95e6c58617fbdf8c125e51a32bef873bdff4d5978ec9ba76e685a67d63
-size 119235
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_349.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_349.py
deleted file mode 100644
index 93427a5161038fcdbc7d78354cd03602e4b8a99d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_349.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_347.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_349.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_349.py.debug_pkl
deleted file mode 100644
index b9581793cd0d26a44f4500810d0da6ef0a8106d5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_349.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d93d4339c2058fe2e90d9a55eadbc0b9ece20c33e2bd4a32108c145a8a58e8da
-size 89580
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_353.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_353.py
deleted file mode 100644
index 4970ec7aaec88019af2a220896ec7db6ab279429..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_353.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_352.MultiheadAttention
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_353.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_353.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_353.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_360.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_360.py
deleted file mode 100644
index 0d253a2f5198753f821e197454215830e3fb3c51..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_360.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_355.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_356.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_357.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_358.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_360.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_360.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_360.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_361.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_361.py
deleted file mode 100644
index df0b8b35129fc3b09e96344e009cd7145881f9f4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_361.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_346.LayerNorm
-  filter : __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_350.Identity
-  attn : __torch__.NO.___torch_mangle_353.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_360.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_361.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_361.py.debug_pkl
deleted file mode 100644
index 803e09d9e031fd494b32489e9462cd302f1d2c1c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_361.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2a8b19bf6b36efbe94d485071bd29f63fdfa79aa426761056dbf030598274e3e
-size 2911
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_365.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_365.py
deleted file mode 100644
index ac9b00c225cb8b3468dda543a4831cadf007f3d6..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_365.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_363.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_365.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_365.py.debug_pkl
deleted file mode 100644
index ca20cd68937acb127770808d7b4fdd1c56c672ed..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_365.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:05ed4d9ceb6334a81163cf82cb24c770bf7f188a7cf65c64bdb56fba170a0eec
-size 89580
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_369.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_369.py
deleted file mode 100644
index ea354395ac35858261c9e45247bc69ea94c9213b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_369.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_368.MultiheadAttention
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_369.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_369.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_369.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_376.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_376.py
deleted file mode 100644
index 8cd56e06836952c6856c4f4a43549a8d788f46f1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_376.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_371.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_372.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_373.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_374.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_376.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_376.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_376.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_377.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_377.py
deleted file mode 100644
index 5c593e4f98e785e3696f76edb033833e9986b118..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_377.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm
-  filter : __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_366.Identity
-  attn : __torch__.NO.___torch_mangle_369.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_370.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_376.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_377.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_377.py.debug_pkl
deleted file mode 100644
index 5b53bcd542e9388866abe8306bfd7571b7067d81..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_377.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2b83dc4162407f3b4a2d04a78b4587cf5ede810b5bd6bee04219a5071ae837d2
-size 2911
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_381.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_381.py
deleted file mode 100644
index 94081617ebe8ce2efb23057ee3eada9555066e80..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_381.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_379.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_381.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_381.py.debug_pkl
deleted file mode 100644
index 1572f2adf4ca51c3cb8f5f1fe1078446d8c20fd9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_381.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2d0963b0cae01d4b12ba14e06e641d8372945021b71075e9c9a3fdf66abd1fa8
-size 89580
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_385.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_385.py
deleted file mode 100644
index c918d30932782cc1a86302c5e88f17395e5e5997..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_385.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_384.MultiheadAttention
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_385.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_385.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_385.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_392.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_392.py
deleted file mode 100644
index 3426c49e4cfc54889c0218f200604e9e6a7cdf84..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_392.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_387.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_389.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_390.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_391.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_392.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_392.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_392.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_393.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_393.py
deleted file mode 100644
index 2fe2a4687953b11ec99a9716f910b3f0d5856136..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_393.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_378.LayerNorm
-  filter : __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_382.Identity
-  attn : __torch__.NO.___torch_mangle_385.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_386.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_392.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_393.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_393.py.debug_pkl
deleted file mode 100644
index b7b57a1439e8e8e5cd1a74c781be5d7387220ed1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_393.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:dd643ad7e8b95381075c32c5bbdcbb9f6cc88a7e558109227c3304746b1bf03c
-size 2911
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_397.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_397.py
deleted file mode 100644
index 7aa84e6a746b76a7fba35773a087f294c5d1ce27..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_397.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_395.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_397.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_397.py.debug_pkl
deleted file mode 100644
index 90b730f048142f59d2b3ba6747007ebae40484ac..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_397.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9e95f4842b3694069bf5f134863db1a11152283c1a4254e955885cf4efb04929
-size 89580
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_401.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_401.py
deleted file mode 100644
index 0c84cadde5817b0812df4b5d4f9307504dab8f97..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_401.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_400.MultiheadAttention
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_401.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_401.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_401.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_408.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_408.py
deleted file mode 100644
index 9506beecb0dc8eb1368432a2b68400347bec393e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_408.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_403.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_404.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_405.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_408.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_408.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_408.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_409.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_409.py
deleted file mode 100644
index 108724813bef0bbdae726138a3de917d9406dfbf..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_409.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_394.LayerNorm
-  filter : __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity
-  attn : __torch__.NO.___torch_mangle_401.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_408.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_409.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_409.py.debug_pkl
deleted file mode 100644
index 64647c24e6d160b6feacd33d09a00d4b175d6a21..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_409.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d1f42231c35a7995a5caa7d822beaf8b1c8650cf9f200e1d08950920cb3af9b1
-size 2911
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_413.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_413.py
deleted file mode 100644
index 4f40234c852a09b04df4e4f647aca080e43c76d7..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_413.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_411.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_413.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_413.py.debug_pkl
deleted file mode 100644
index 86ec20efbe0cd19b729919679b211b2cd0196c5c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_413.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:44ecf420b0e44db232e66f60d4622bc185a45d0e55ea9936128bc623ae232dee
-size 89580
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_417.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_417.py
deleted file mode 100644
index 8e065aab89d3135e5450a460a849a4659cf9d427..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_417.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_416.MultiheadAttention
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_417.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_417.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_417.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_424.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_424.py
deleted file mode 100644
index 6af7f16c1641c27928bcea46b483ba498d9d169d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_424.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_420.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_421.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_424.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_424.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_424.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_425.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_425.py
deleted file mode 100644
index e45c3f8e05f9272c3aa3815af13a9f9c1c9e1aaf..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_425.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm
-  filter : __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity
-  attn : __torch__.NO.___torch_mangle_417.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_418.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_424.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_425.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_425.py.debug_pkl
deleted file mode 100644
index 4ed74d306366eaf2df1532d3ca3bd42966cae23f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_425.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:045c0779f7534049676605aaf212b57a85f8caaf8b59408081230e5969bd3fe3
-size 2911
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_429.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_429.py
deleted file mode 100644
index b7c735ad9940ac3f25c60ccae0250c587b86bda2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_429.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_427.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_429.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_429.py.debug_pkl
deleted file mode 100644
index 014999967c3f373f5e499d816bbf1b02999efc42..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_429.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:68a120cb352573fa695b3ce2ecf4a89943cf789ca801393bfecbea7e94930252
-size 89580
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_433.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_433.py
deleted file mode 100644
index f4546a26b39d44e6215e6a1c45236858ff41606e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_433.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_432.MultiheadAttention
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_433.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_433.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_433.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_440.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_440.py
deleted file mode 100644
index cabcaf66e8d427da2b7950fb2a7617a27e04426e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_440.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_435.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_436.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_437.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_438.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_440.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_440.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_440.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_441.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_441.py
deleted file mode 100644
index a2da189f77e0e1617dcb1aa637a403fc569985d4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_441.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_426.LayerNorm
-  filter : __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity
-  attn : __torch__.NO.___torch_mangle_433.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_434.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_440.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_441.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_441.py.debug_pkl
deleted file mode 100644
index b032d76c5549a28a2afc51bb9c8e2950848b517b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_441.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5239c3c0ed508c2a5b9e625f481cb5e3a821a122e759a7ef33589114450781a7
-size 2911
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_445.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_445.py
deleted file mode 100644
index 6a9dac11b3289499072ad8ccbbf0495b9eea8e1a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_445.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_443.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_445.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_445.py.debug_pkl
deleted file mode 100644
index df50042564c7dd8b5841743adba64b5d5d5c017a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_445.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:444594cb20903e8e5f96ea697f760dd821ea8da1e324c6166c0ec6fdc81fa983
-size 89580
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_449.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_449.py
deleted file mode 100644
index 2432a75247f8a0da5f9216e39c65b0a9cd5be3fe..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_449.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_448.MultiheadAttention
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_449.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_449.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_449.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_456.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_456.py
deleted file mode 100644
index 6843eb7c1a066c02cf3a6e938d16fa42e8bb6e45..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_456.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_451.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_452.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_453.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_454.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_456.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_456.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_456.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_457.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_457.py
deleted file mode 100644
index c55e7606dfe69502e136f7147e5d77e076ad281e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_457.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_442.LayerNorm
-  filter : __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_446.Identity
-  attn : __torch__.NO.___torch_mangle_449.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_450.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_456.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_457.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_457.py.debug_pkl
deleted file mode 100644
index 27bd6495e22e96b98bce7137a5805283c617f851..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_457.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9567f242238809585285fc78d790cc6ef318433ec853c5d721abb66942469a71
-size 2911
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_461.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_461.py
deleted file mode 100644
index 1ecafd2a33d2736efc15e5bc423fb54f8aca80a4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_461.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_459.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_461.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_461.py.debug_pkl
deleted file mode 100644
index 2ea9f266b1fc4c7f655c7ee55969f0740690ac86..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_461.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:da8f8a695c59762b8883ecf47beafa5a4d47e38976a36bf5cf3491ce523edc10
-size 89580
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_465.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_465.py
deleted file mode 100644
index 51026ab5508fc30ede9740277a8936d411133e9b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_465.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_464.MultiheadAttention
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_465.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_465.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_465.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_472.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_472.py
deleted file mode 100644
index f8c29dbe815fed9f8aac611ba8dd03c5ec883f5f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_472.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_467.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_468.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_469.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_470.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_471.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_472.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_472.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_472.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_473.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_473.py
deleted file mode 100644
index 3f8c24f9b3c4411c54b346cf5be73133e0c2255c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_473.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_458.LayerNorm
-  filter : __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_462.Identity
-  attn : __torch__.NO.___torch_mangle_465.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_466.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_472.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_473.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_473.py.debug_pkl
deleted file mode 100644
index 5e780274f3faca7bdd293baca5d62ea9a3c3b98b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_473.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:459d7f1eb093044f413bc8ca70e2e65d0e1e054a067383ff25ceb11f8d7da1e6
-size 2911
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_477.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_477.py
deleted file mode 100644
index 814cbc85a36c97da1255b60c32598539be4faad9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_477.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_475.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_477.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_477.py.debug_pkl
deleted file mode 100644
index 41515e94980b76684596ac518957e60859ee00f2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_477.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:734c71ffd3ea6930f590e6ba040c77e7071b5db70113221802d800a8ff97107a
-size 89580
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_481.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_481.py
deleted file mode 100644
index b7875f2381497446d1385d7a7c6f569e6a67e0e1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_481.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_480.MultiheadAttention
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_481.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_481.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_481.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_488.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_488.py
deleted file mode 100644
index 9258f7a6d0eaed128a862b0d81a8e28711e13a02..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_488.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_483.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_484.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_485.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_486.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_487.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_488.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_488.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_488.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_489.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_489.py
deleted file mode 100644
index e5e30c6bab14c47d0128ecefabfe5758949de3e3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_489.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_474.LayerNorm
-  filter : __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_478.Identity
-  attn : __torch__.NO.___torch_mangle_481.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_482.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_488.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_489.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_489.py.debug_pkl
deleted file mode 100644
index c185403f9053877b5981afec18ead06d511320ec..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_489.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b345868e6078d921bb9a027bcde015767e829b7bf8b2fe4ba61b597597fee9d5
-size 2911
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_493.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_493.py
deleted file mode 100644
index 99b86c5edabff184ce203226f10c256c6b43bc3b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_493.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_491.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_493.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_493.py.debug_pkl
deleted file mode 100644
index 7ada45e396d1c3fb3a0caa0fceb8ba1a2e2555b9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_493.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8f44d376d4d0ac2f66d1c9a57da17bc88b3df9a798f859ad8833ec4638de6efb
-size 89580
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_497.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_497.py
deleted file mode 100644
index f2c0b0958e7c681905129bc68a122fd21adb7dd9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_497.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_496.MultiheadAttention
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_497.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_497.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_497.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_504.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_504.py
deleted file mode 100644
index 2ce55548d3471efd665e2fdc94091e2d9b43190e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_504.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_499.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_501.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_502.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_503.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_504.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_504.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_504.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_505.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_505.py
deleted file mode 100644
index 2f82135d17cd39c5ffedb478e96a8649283e134e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_505.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_490.LayerNorm
-  filter : __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity
-  attn : __torch__.NO.___torch_mangle_497.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_498.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_504.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_505.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_505.py.debug_pkl
deleted file mode 100644
index 0f0dba1bee6d0bc10fb02204eca1cfa3bdcc41f0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_505.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7cda87e88a153b71f6f5ea8ccdaca61b67d3fb1253e0cbe9d0fe31425bef0045
-size 2911
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_509.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_509.py
deleted file mode 100644
index 422e701240a1c1c577b8210dbb8843434f408877..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_509.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_507.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_509.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_509.py.debug_pkl
deleted file mode 100644
index b88eb1b97781a58e63099cbe9a0c88a7188e50da..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_509.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2437366f9c65fdb0f59c1290f8d31d7d97f3bcbfb36e0c588894516691ebe8d7
-size 89580
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_513.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_513.py
deleted file mode 100644
index e343f5408feb0e565cfc9d80fadd35d75a450aae..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_513.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_512.MultiheadAttention
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_513.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_513.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_513.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_520.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_520.py
deleted file mode 100644
index c0e9f3b6e0810ddce1199b2f8e2b78177a7da1e5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_520.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_515.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_517.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_518.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_519.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_520.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_520.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_520.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_521.py b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_521.py
deleted file mode 100644
index dc7c9b758b44e1b522d788691f24c9bc49a3968b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_521.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_506.LayerNorm
-  filter : __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_510.Identity
-  attn : __torch__.NO.___torch_mangle_513.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_514.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_520.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_521.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_521.py.debug_pkl
deleted file mode 100644
index c4295d0cbbe24538317ac254e47f335b626262d1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/NO/___torch_mangle_521.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c4d45d921b793f9f722cf20eae2951a54f26a13ac821c3d393461d3c8d340d6b
-size 2911
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock.py b/xe_weights14/glonet_p1/code/__torch__/dblock.py
deleted file mode 100644
index 17b7bcea8e379b9efe2204d2834439fa35070bcb..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock.py
+++ /dev/null
@@ -1,160 +0,0 @@
-class TeDev(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_541.LayerNorm
-  enc : __torch__.torch.nn.modules.container.___torch_mangle_692.Sequential
-  blocks : __torch__.torch.nn.modules.container.___torch_mangle_846.ModuleList
-  dec : __torch__.torch.nn.modules.container.___torch_mangle_999.Sequential
-  def forward(self: __torch__.dblock.TeDev,
-    x: Tensor) -> Tensor:
-    dec = self.dec
-    _7 = getattr(dec, "7")
-    dec0 = self.dec
-    _6 = getattr(dec0, "6")
-    dec1 = self.dec
-    _5 = getattr(dec1, "5")
-    dec2 = self.dec
-    _4 = getattr(dec2, "4")
-    dec3 = self.dec
-    _3 = getattr(dec3, "3")
-    dec4 = self.dec
-    _2 = getattr(dec4, "2")
-    dec5 = self.dec
-    _1 = getattr(dec5, "1")
-    dec6 = self.dec
-    _0 = getattr(dec6, "0")
-    norm = self.norm
-    blocks = self.blocks
-    _11 = getattr(blocks, "11")
-    blocks0 = self.blocks
-    _10 = getattr(blocks0, "10")
-    blocks1 = self.blocks
-    _9 = getattr(blocks1, "9")
-    blocks2 = self.blocks
-    _8 = getattr(blocks2, "8")
-    blocks3 = self.blocks
-    _70 = getattr(blocks3, "7")
-    blocks4 = self.blocks
-    _60 = getattr(blocks4, "6")
-    blocks5 = self.blocks
-    _50 = getattr(blocks5, "5")
-    blocks6 = self.blocks
-    _40 = getattr(blocks6, "4")
-    blocks7 = self.blocks
-    _30 = getattr(blocks7, "3")
-    blocks8 = self.blocks
-    _20 = getattr(blocks8, "2")
-    blocks9 = self.blocks
-    _12 = getattr(blocks9, "1")
-    blocks10 = self.blocks
-    _00 = getattr(blocks10, "0")
-    enc = self.enc
-    _71 = getattr(enc, "7")
-    enc0 = self.enc
-    _61 = getattr(enc0, "6")
-    enc1 = self.enc
-    _51 = getattr(enc1, "5")
-    enc2 = self.enc
-    _41 = getattr(enc2, "4")
-    enc3 = self.enc
-    _31 = getattr(enc3, "3")
-    enc4 = self.enc
-    _21 = getattr(enc4, "2")
-    enc5 = self.enc
-    _13 = getattr(enc5, "1")
-    enc6 = self.enc
-    _01 = getattr(enc6, "0")
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _14 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _15 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _16 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _17 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _18 = int(W)
-    _19 = [_14, int(torch.mul(T, C)), _17, _18]
-    input = torch.reshape(x, _19)
-    _22 = (_01).forward(input, )
-    _23 = (_13).forward(_22, )
-    _24 = (_21).forward(_23, )
-    _25 = (_31).forward(_24, )
-    _26 = (_41).forward(_25, )
-    _27 = (_51).forward(_26, )
-    _28 = (_61).forward(_27, )
-    _29 = (_71).forward(_28, )
-    B0 = ops.prim.NumToTensor(torch.size(_29, 0))
-    _32 = int(B0)
-    _33 = int(B0)
-    _34 = int(B0)
-    D = ops.prim.NumToTensor(torch.size(_29, 1))
-    _35 = int(D)
-    _36 = int(D)
-    H0 = ops.prim.NumToTensor(torch.size(_29, 2))
-    _37 = int(H0)
-    _38 = int(H0)
-    W0 = ops.prim.NumToTensor(torch.size(_29, 3))
-    _39 = int(W0)
-    _42 = int(W0)
-    N = torch.mul(H0, W0)
-    _43 = int(N)
-    z = torch.contiguous(torch.permute(_29, [0, 2, 3, 1]))
-    input0 = torch.view(z, [_34, _43, _36])
-    _44 = (_12).forward((_00).forward(input0, ), )
-    _45 = (_30).forward((_20).forward(_44, ), )
-    _46 = (_50).forward((_40).forward(_45, ), )
-    _47 = (_70).forward((_60).forward(_46, ), )
-    _48 = (_10).forward((_9).forward((_8).forward(_47, ), ), )
-    _49 = (norm).forward((_11).forward(_48, ), )
-    z0 = torch.contiguous(torch.permute(_49, [0, 2, 1]))
-    input1 = torch.reshape(z0, [_33, _35, _38, _42])
-    input2 = torch.cat([(_0).forward(input1, ), _28], 1)
-    input3 = torch.cat([(_1).forward(input2, ), _27], 1)
-    input4 = torch.cat([(_2).forward(input3, ), _26], 1)
-    input5 = torch.cat([(_3).forward(input4, ), _25], 1)
-    input6 = torch.cat([(_4).forward(input5, ), _24], 1)
-    input7 = torch.cat([(_5).forward(input6, ), _23], 1)
-    input8 = torch.cat([(_6).forward(input7, ), _22], 1)
-    y = torch.reshape((_7).forward(input8, ), [_32, _15, _16, _37, _39])
-    return torch.add(y, x)
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_542.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_558.Sequential
-  def forward(self: __torch__.dblock.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _30 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_30, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_30, ))
-    y1 = torch.add_(y0, (_2).forward(_30, ))
-    return torch.add_(y1, (_3).forward(_30, ))
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_543.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_544.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_545.LeakyReLU
-  def forward(self: __torch__.dblock.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _31 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_31, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock.py.debug_pkl
deleted file mode 100644
index 87e65dbdf36a0434d6f335781a69dcd57a49e8cc..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:04f2f52655dd6b29d4be923e686162928fbed611e47396a7fd89a6bf5d4419e9
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_549.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_549.py
deleted file mode 100644
index 0926398c6f28a7475e4ff8016ca5554166e16639..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_549.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_546.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_547.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_548.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_549.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_549.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_549.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_549.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_553.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_553.py
deleted file mode 100644
index 8f9ba243b10b7efaba9b158d3076528e14ffc303..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_553.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_550.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_551.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_552.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_553.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_553.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_553.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_553.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_557.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_557.py
deleted file mode 100644
index c9638ad03090a62cdb9b76a1df7ed0e56135d11a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_557.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_554.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_555.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_556.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_557.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_557.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_557.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_557.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_563.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_563.py
deleted file mode 100644
index 14f10363b3ff5ffed26868ab0af139bef8af4f0a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_563.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_560.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_561.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_562.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_563.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_563.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_563.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_563.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_567.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_567.py
deleted file mode 100644
index 5a87dd1c12757d9cb1da748ec7e6ff22788d18d4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_567.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_564.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_565.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_566.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_567.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_567.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_567.py.debug_pkl
deleted file mode 100644
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+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_571.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_571.py
deleted file mode 100644
index f1a8c968331a80cc08230816c10cd3476f1429d2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_571.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_568.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_569.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_570.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_571.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_571.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_571.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_571.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_575.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_575.py
deleted file mode 100644
index afb49821abda9f44f21c8b0224d54a548f7df894..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_575.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_572.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_573.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_574.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_575.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_575.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_575.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_575.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_577.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_577.py
deleted file mode 100644
index b308374844388faba3c28fa7b6e7b4925de74ed4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_577.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_559.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_576.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_577.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_577.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_577.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_577.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_582.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_582.py
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index 98d4879266bdf52adfb68ed65c7f9dfd5ffc4958..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_582.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_579.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_580.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_581.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_582.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_582.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_582.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_582.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_586.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_586.py
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index 0af0c27e64b64f1b31bfbb5de58fadd6f743cd91..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_586.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_583.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_584.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_585.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_586.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_586.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_586.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_586.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_590.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_590.py
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_590.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_587.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_588.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_589.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_590.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_590.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_590.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_590.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_594.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_594.py
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_594.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_591.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_592.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_593.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_594.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_594.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_594.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_594.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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index be847eae65500962129005fc29e9a749f8038111..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_596.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_578.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_595.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_596.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_596.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_596.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_596.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_601.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_601.py
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_601.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_598.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_599.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_600.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_601.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_601.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_601.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_601.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_605.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_605.py
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_605.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_602.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_603.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_604.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_605.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_605.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_605.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_605.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_609.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_606.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_607.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_608.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_609.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_609.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_609.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_613.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_610.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_611.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_612.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_613.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_613.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_613.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_613.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_615.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_615.py
deleted file mode 100644
index ea5ee10e7a6b57918a3acd179b41acf48600f529..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_615.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_597.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_614.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_615.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_615.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_615.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_615.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_620.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_620.py
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index e14d4070a7bca2f8fbf5666f13495ff02a6c1f88..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_620.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_617.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_618.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_619.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_620.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_620.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_620.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_620.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_624.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_621.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_622.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_623.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_624.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
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-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_624.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_624.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_628.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_625.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_626.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_627.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_628.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
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-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_628.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_628.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_632.py
+++ /dev/null
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-class GroupConv2d(Module):
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_629.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_630.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_631.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_632.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
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-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_632.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_632.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_634.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_616.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_633.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_634.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
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-    lls0 = self.lls
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-    lls1 = self.lls
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-    lls2 = self.lls
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-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_634.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_634.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_639.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_636.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_637.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_638.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_639.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
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-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_639.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_639.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_643.py
+++ /dev/null
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-class GroupConv2d(Module):
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-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_641.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_642.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_643.GroupConv2d,
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-    activate = self.activate
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-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_643.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_643.py.debug_pkl
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+++ /dev/null
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-class GroupConv2d(Module):
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-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_645.GroupNorm
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-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_647.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_647.py.debug_pkl
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-class GroupConv2d(Module):
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-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_650.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_651.GroupConv2d,
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-    activate = self.activate
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-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_651.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_651.py.debug_pkl
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-class Inception(Module):
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-  lls : __torch__.torch.nn.modules.container.___torch_mangle_652.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_653.Inception,
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-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_653.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_653.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_658.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_655.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_656.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_657.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_658.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_658.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_658.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_658.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_662.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_662.py
deleted file mode 100644
index 280fb2e1822d38d1c8e575a3dbaf5dd870b6d730..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_662.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_659.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_660.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_661.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_662.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_662.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_662.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_662.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_666.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_666.py
deleted file mode 100644
index c6c5e4489bf00282ce6c68e207aade2d6866cdb7..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_666.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_663.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_664.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_665.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_666.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_666.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_666.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_666.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_670.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_670.py
deleted file mode 100644
index cf69c8ac9e04a78d8de1b3053a9587ea5a7d98c0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_670.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_667.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_668.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_669.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_670.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_670.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_670.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_670.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_672.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_672.py
deleted file mode 100644
index 0d15292de2dc1f5d3f1cb813f78f1c51532cc521..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_672.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_654.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_671.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_672.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_672.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_672.py.debug_pkl
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index a92e7c8e2f401145ae2d9bf2b8b580f52d3bd2aa..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_672.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_677.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_677.py
deleted file mode 100644
index adfd79947e903a2d5689253faf6bc1726d460677..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_677.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_674.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_675.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_676.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_677.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_677.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_677.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_677.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_681.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_681.py
deleted file mode 100644
index ea0bc5806f156b04e1cfeca3819c126b3876011e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_681.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_678.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_679.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_680.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_681.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_681.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_681.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_681.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_685.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_685.py
deleted file mode 100644
index 9ff137a038baf2fc57cbbae0c5bcaf992001b2af..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_685.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_682.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_683.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_684.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_685.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_685.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_685.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_685.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_689.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_689.py
deleted file mode 100644
index a4704ed24c701f9b4a3d088438eaf10b819b8934..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_689.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_686.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_687.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_688.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_689.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_689.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_689.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_689.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_691.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_691.py
deleted file mode 100644
index 4072e9b6786707376a3559efccad9e83727d0d86..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_691.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_673.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_690.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_691.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_691.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_691.py.debug_pkl
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index 313eae611d09f7f5880a4777fcface6cf6fe1c75..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_691.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_851.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_851.py
deleted file mode 100644
index 96b36a3dfebc3573782e4935712ef6c53ef7bc46..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_851.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_848.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_849.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_850.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_851.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_851.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_851.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_851.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_855.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_855.py
deleted file mode 100644
index ee2e75d0ed1329ede6b8c0350de0d50e81cea2da..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_855.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_852.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_853.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_854.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_855.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_855.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_855.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_855.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_859.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_859.py
deleted file mode 100644
index de8e67fe980586dc919fb6eb096eb3f3c3cb443e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_859.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_856.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_857.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_858.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_859.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_859.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_859.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_859.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_863.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_863.py
deleted file mode 100644
index 0b6c1e696d151fe284eb83585d67e87d3f87e8b0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_863.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_860.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_861.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_862.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_863.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_863.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_863.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_863.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_865.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_865.py
deleted file mode 100644
index b2a1efb57079c24bedbd1736fa84ede97fb8c878..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_865.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_847.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_864.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_865.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_865.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_865.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_865.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_870.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_870.py
deleted file mode 100644
index 503c7de5f5c95004e7cb113a89f3264df66c2295..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_870.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_867.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_868.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_869.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_870.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_870.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_870.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_870.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_874.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_874.py
deleted file mode 100644
index 6a0ace4cec97e9d53cdb5c1941ffcec619fc0d19..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_874.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_871.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_872.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_873.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_874.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_874.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_874.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_874.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_878.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_878.py
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index 094d6182b83d1a0943059feb5d80ed188908c6c4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_878.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_875.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_876.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_877.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_878.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_878.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_878.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_878.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_882.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_882.py
deleted file mode 100644
index 4e29a152a94094bcffc007208b1ce2ca9528c08e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_882.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_879.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_880.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_881.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_882.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_882.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_882.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_882.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_884.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_884.py
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index 8f38d0d51b5d8bb9d7f2651394e8d7d7501538dd..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_884.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_866.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_883.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_884.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_884.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_884.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_884.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_889.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_889.py
deleted file mode 100644
index 538e0ffe9bd70126a1ef16b86ed8a7c36bfd9425..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_889.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_886.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_887.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_888.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_889.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_889.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_889.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_889.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_893.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_893.py
deleted file mode 100644
index 22ff9a3c5602b0cc8e17b636ec73b6a8000146cc..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_893.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_890.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_891.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_892.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_893.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_893.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_893.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_893.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_897.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_897.py
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index b2f4f65dfe1133a30d581855d308bb7ec39ec279..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_897.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_894.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_895.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_896.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_897.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_897.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_897.py.debug_pkl
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+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_901.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_901.py
deleted file mode 100644
index ac540d7f8ec486e406af35314e033e6889aadc2b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_901.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_898.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_899.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_900.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_901.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_901.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_901.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_901.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_903.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_903.py
deleted file mode 100644
index 30aec46015da8a2c4dd4bb4ada306845f2fe8f16..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_903.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_885.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_902.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_903.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_903.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_903.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_903.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_908.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_908.py
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_908.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_905.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_906.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_907.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_908.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_908.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_908.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_908.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_912.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_912.py
deleted file mode 100644
index b994569ff3a60346240199dbbc625411b97a6937..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_912.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_909.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_910.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_911.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_912.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
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-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_912.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_912.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_912.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_916.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_913.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_914.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_915.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_916.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_916.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_916.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_916.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_920.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_917.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_918.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_919.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_920.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_920.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_920.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_920.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_922.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_904.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_921.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_922.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
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-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_922.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_922.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_927.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_924.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_925.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_926.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_927.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
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-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_927.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_927.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_931.py
+++ /dev/null
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-class GroupConv2d(Module):
-  __parameters__ = []
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-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_929.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_930.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_931.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
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-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_931.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_931.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_931.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_935.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-    activate = self.activate
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-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_935.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_935.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_939.py
+++ /dev/null
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-class GroupConv2d(Module):
-  __parameters__ = []
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-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_938.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_939.GroupConv2d,
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-    activate = self.activate
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-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_939.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_939.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_941.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
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-  lls : __torch__.torch.nn.modules.container.___torch_mangle_940.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_941.Inception,
-    input: Tensor) -> Tensor:
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-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_941.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_941.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_946.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_946.py
deleted file mode 100644
index 4237ab7951dfb55cd3d11aa2636abdebf63cc99e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_946.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_943.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_944.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_945.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_946.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_946.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_946.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_946.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_950.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_950.py
deleted file mode 100644
index 732b247ca727176a554611fff75cf0acd9c286e8..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_950.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_947.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_948.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_949.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_950.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_950.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_950.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_950.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_954.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_954.py
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_954.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_951.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_952.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_953.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_954.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_954.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_954.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_954.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_958.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_958.py
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_958.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_955.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_956.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_957.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_958.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_958.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_958.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_958.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_960.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_960.py
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_960.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_942.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_959.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_960.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_960.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_960.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_960.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_965.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_965.py
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_965.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_962.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_963.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_964.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_965.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_965.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_965.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_965.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_969.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_969.py
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_969.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_966.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_967.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_968.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_969.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_969.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_969.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_969.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_973.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_973.py
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_973.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_970.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_971.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_972.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_973.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_973.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_973.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_977.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_977.py
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_977.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_974.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_975.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_976.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_977.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
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-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_977.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_977.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_979.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_979.py
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_979.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_961.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_978.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_979.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
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-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_979.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_979.py.debug_pkl
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+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_984.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_984.py
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_984.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_981.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_982.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_983.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_984.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_984.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_984.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_988.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_988.py
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--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_988.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_985.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_986.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_987.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_988.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_988.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_988.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_988.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
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diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_992.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_992.py
deleted file mode 100644
index 3a668cf2fe683c65a7ab0d85d5d9e006f9a85135..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_992.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_989.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_990.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_991.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_992.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_992.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_992.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_992.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_996.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_996.py
deleted file mode 100644
index 0fc7b031dfb8890175beff5b40570a464b2d5d2a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_996.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_993.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_994.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_995.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_996.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_996.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_996.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_996.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_998.py b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_998.py
deleted file mode 100644
index cba35dd15f504e961e33b5462b147e234737241f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_998.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_980.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_997.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_998.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_998.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_998.py.debug_pkl
deleted file mode 100644
index b4fdcb5a14ad60cdb77e61af3beea7e8a8ab451a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/dblock/___torch_mangle_998.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:565d092d53fb4c7776e470bf9e3ce50c4e3e4a97830841b8c607ee48f2ed431d
-size 1770
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu.py b/xe_weights14/glonet_p1/code/__torch__/fcu.py
deleted file mode 100644
index b73075e7f2aaedb75cc7daec2932a8bdeee0c363..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu.py
+++ /dev/null
@@ -1,107 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_693.LayerNorm
-  filter : __torch__.fcu.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_696.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_697.LayerNorm
-  mlp : __torch__.fcu.Mlp
-  def forward(self: __torch__.fcu.FourierNetBlock,
-    input: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(input, ), )
-    _1 = (drop_path).forward()
-    input0 = torch.add(input, _0)
-    _2 = (mlp).forward((normlayer2).forward(input0, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input0, _2)
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_694.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_695.Conv1d
-  def forward(self: __torch__.fcu.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _4 = int(B)
-    _5 = int(B)
-    _6 = int(B)
-    _7 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _8 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _9 = int(C)
-    _10 = int(C)
-    _11 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_11)
-    _12 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_12)
-    x = torch.reshape(argument_1, [_7, 336, 720, _10])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _13 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _14 = int(_13)
-    _15 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_6, _14, int(_15), 2, 64])
-    _16 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _17 = torch.einsum("...bd, bdk->...bk", _16)
-    _18 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _19 = torch.einsum("...bd, bdk->...bk", _18)
-    input1 = torch.add(torch.sub(_17, _19), torch.select(b1, 0, 0))
-    _20 = torch.relu_(input1)
-    _21 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _22 = torch.einsum("...bd, bdk->...bk", _21)
-    _23 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _24 = torch.einsum("...bd, bdk->...bk", _23)
-    input2 = torch.add(torch.add(_22, _24), torch.select(b1, 0, 1))
-    _25 = torch.relu_(input2)
-    _26 = torch.einsum("...bd, bdk->...bk", [_20, torch.select(w2, 0, 0)])
-    _27 = torch.einsum("...bd, bdk->...bk", [_25, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_26, _27), torch.select(b2, 0, 0))
-    _28 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _29 = torch.einsum("...bd, bdk->...bk", [_25, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_28, _29), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _30 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _31 = int(_30)
-    _32 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_5, _31, int(_32), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_4, _8, _9])
-    return torch.add(x6, bias0)
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_698.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_699.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_700.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_701.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_702.Dropout
-  def forward(self: __torch__.fcu.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _33 = (act).forward((fc1).forward(argument_1, ), )
-    _34 = (fc3).forward((drop).forward(_33, ), )
-    return (drop).forward1(_34, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu.py.debug_pkl
deleted file mode 100644
index 03588d10598f454711158fd022bf576997d11899..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:32030204f7cef6ff9298aef2ec99eb7ef827faee98d2715af68140721a11542f
-size 13847
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_706.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_706.py
deleted file mode 100644
index 24f11bfb5025bebe77c9e77e87c687f285e6e103..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_706.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_704.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_705.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_706.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_706.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_706.py.debug_pkl
deleted file mode 100644
index e93b5fb0a62709b605e6edb750fbc9e6ea16ca64..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_706.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d90063a87a1f9720441f5b2b4eb71b4b2ce5639513ad8cbfd4fd867b12cc3082
-size 13613
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_714.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_714.py
deleted file mode 100644
index 2cd22cbfc39c1b63f28d2a86a80d962c0b657d43..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_714.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_709.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_710.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_711.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_712.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_713.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_714.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_714.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_714.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_714.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_715.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_715.py
deleted file mode 100644
index 8e46abe883d9c4e5bcc080705ae394534ba35056..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_715.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_703.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_706.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_707.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_708.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_714.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_715.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_715.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_715.py.debug_pkl
deleted file mode 100644
index 96011915595891bdb0efa16d2dd54cb386eafe97..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_715.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6a82176f38b067115a7be1c49e5ed12ea90ed14c9f742f08bacde9d1965fe341
-size 1495
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_719.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_719.py
deleted file mode 100644
index 3b9fc1085a22df614d1a22642d8177685bab0c0b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_719.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_717.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_718.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_719.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_719.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_719.py.debug_pkl
deleted file mode 100644
index b347084764fb88b29ef8d8704de9b82f7a3afd7a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_719.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:76243ed2534795c757674ae39d4c08935be21eec34a9d18a34e8acb8d2795e81
-size 13613
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_727.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_727.py
deleted file mode 100644
index aa5499a7f950cc4c15758302f29e731d7358ab09..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_727.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_722.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_723.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_724.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_725.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_726.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_727.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_727.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_727.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_727.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_728.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_728.py
deleted file mode 100644
index 142cc83f4021c365891644552c9178b438051778..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_728.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_716.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_719.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_720.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_721.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_727.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_728.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_728.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_728.py.debug_pkl
deleted file mode 100644
index 24da62846457b3ddad7752ea17487d7b212ca341..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_728.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f89235890ac5513a6780c092e51b18720993b3373727cd03efc524805740e4d5
-size 1495
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_732.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_732.py
deleted file mode 100644
index 31003d6d7276b43cc7a672c7fce5cfd88bcdb966..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_732.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_730.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_731.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_732.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_732.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_732.py.debug_pkl
deleted file mode 100644
index f6cd7b100af3848a0e9de2e93ea4b739910a8eaa..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_732.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f3d93f338212cf078fbe2886f6aa8587b7111fc22ccaafe495072a3d2a02470e
-size 13613
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_740.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_740.py
deleted file mode 100644
index 930a786a4bc0008ff13ca06df553fd401494f368..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_740.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_735.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_736.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_737.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_738.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_739.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_740.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_740.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_740.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_740.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_741.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_741.py
deleted file mode 100644
index bc5fb45af290e57386e00835a605f429a79c75d1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_741.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_729.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_732.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_733.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_734.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_740.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_741.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_741.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_741.py.debug_pkl
deleted file mode 100644
index 5a472e25faf73af758bca1185ec07d43ce3e2a68..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_741.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:07650d3befc730f28adfe8f5be55e10ed76e18ba5e1a5cf11632b12b7b36f60a
-size 1495
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_745.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_745.py
deleted file mode 100644
index cd9b935344c492e54e654e80afffc77a93114a1c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_745.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_743.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_744.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_745.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_745.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_745.py.debug_pkl
deleted file mode 100644
index 9fd3d6d619c0569d078abf046a007e30366dc01b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_745.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e8376f3fda497cdcfea560f105d857162fa2956aa3aa5819b0f6fc5cf7c2a5e8
-size 13613
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_753.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_753.py
deleted file mode 100644
index bb0c094493892bc72f64fe4fd39ca728beb9edc7..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_753.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_748.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_749.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_750.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_751.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_752.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_753.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_753.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_753.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_753.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_754.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_754.py
deleted file mode 100644
index 7a2d10f6f720b0e6b3a0e9b7ecf6b00863b05f22..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_754.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_742.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_745.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_746.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_747.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_753.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_754.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_754.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_754.py.debug_pkl
deleted file mode 100644
index 2bfacbeea41bcefef5d388df525eb4c2dc80341c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_754.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:75ce7bbd659cacd54ef3d7573bb12020a11e0c0a40bd1e5c7f26d473cfd61a80
-size 1495
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_758.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_758.py
deleted file mode 100644
index d3d7ddda308f7572872064923a93be63162cf6bc..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_758.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_756.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_757.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_758.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_758.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_758.py.debug_pkl
deleted file mode 100644
index 474b960a14ab275d527527f4c60fefdf4162a3ca..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_758.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:dbb795ec210813fc9b8f97f82838c97591d2fcdc652c13d4d3a194cfbfdb22a4
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diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_766.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_766.py
deleted file mode 100644
index a3a6ec04b5aa842b4396cb9675fdb6476e1fb835..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_766.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_761.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_762.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_763.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_764.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_765.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_766.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_766.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_766.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_766.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_767.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_767.py
deleted file mode 100644
index 2dda3658bf454e45c5ba3f96823f3af409724d72..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_767.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_755.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_758.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_759.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_760.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_766.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_767.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_767.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_767.py.debug_pkl
deleted file mode 100644
index fcf6e5d8dbae12fb33afd249dbfb825f45d52b12..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_767.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3af074f47cbddd78d2d1ee7003f2d592d25fdb0b8ed72cc467025aef22197b46
-size 1495
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_771.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_771.py
deleted file mode 100644
index 525b195fb129279799e074d74e36a2fe022c15b3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_771.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_769.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_770.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_771.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_771.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_771.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_771.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_779.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_779.py
deleted file mode 100644
index 5cbcb039fd1fc5d41334721dfb5a8fdde0bee381..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_779.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_774.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_775.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_776.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_777.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_778.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_779.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_779.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_779.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_779.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
index 3b713c8e45d2c190ca664474ab30ddfe1fabc5f8..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_780.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_768.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_771.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_772.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_773.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_779.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_780.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_780.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_780.py.debug_pkl
deleted file mode 100644
index 1095bdd5da294bce9c47a733a0e17d0e247e4289..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_780.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:51db622ce46a1a6ab33481fefe7c99e7fb74dccf560868bef00f109b7e449132
-size 1495
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_784.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_784.py
deleted file mode 100644
index 590cdb028d7ac43be1a3ccd854986437fb08d521..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_784.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_782.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_783.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_784.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_784.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_784.py.debug_pkl
deleted file mode 100644
index 4157def4af6fcb6b75cfee3dfa631914367c17fa..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_784.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:78eddbda7e56d5100dda43c80eb5714d4ab98f5dae9d959139723b84bd04a9d6
-size 13613
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_792.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_792.py
deleted file mode 100644
index 5fbb5fe82bc52bdf3fb1ead0ff698d44340b8488..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_792.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_787.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_788.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_789.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_790.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_791.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_792.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_792.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_792.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_792.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_793.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_793.py
deleted file mode 100644
index 2382fc7122f1053c5461bc3be1dc74dbf3803e62..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_793.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_781.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_784.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_785.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_786.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_792.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_793.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_793.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_793.py.debug_pkl
deleted file mode 100644
index 57a97b1e150090026aa5da551a6f0d255af5d732..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_793.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:93b52727919355d6fef92a69602d8fc117e92bbcf4c24697d8983d9c51f21422
-size 1495
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_797.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_797.py
deleted file mode 100644
index 9dfe5fe9ec9c3a9e42cdaa91704662dcf5030582..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_797.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_795.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_796.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_797.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_797.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_797.py.debug_pkl
deleted file mode 100644
index db7cca8f04d94075f9e8e5180a6211860152678a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_797.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:cda830f9b8224f241ac4fb7c2e4b0bae2a62ee925d010e9ff89f4777ddae0736
-size 13613
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_805.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_805.py
deleted file mode 100644
index 0ccc6e2c2d390b2eed5c153f60ff638b5e481500..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_805.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_800.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_801.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_802.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_803.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_804.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_805.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_805.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_805.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_805.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_806.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_806.py
deleted file mode 100644
index f2a50da19933288ba8096e0be7423cb19e5cb6cf..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_806.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_794.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_797.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_798.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_799.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_805.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_806.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_806.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_806.py.debug_pkl
deleted file mode 100644
index be733601b17e2cafda8326b64c8b29a41abbee13..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_806.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:dd58eb2bcfb6bdb622f4eff1ce38136319701b890bac62da795ba48d6f08f2e0
-size 1495
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_810.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_810.py
deleted file mode 100644
index 09cfff346f1441730b09f973d3203e979df96920..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_810.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_808.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_809.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_810.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_810.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_810.py.debug_pkl
deleted file mode 100644
index 0946c5844ed51d0b93c6f6c217ba1e88a4a7f4b7..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_810.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7e81904b7cbb5952da5fd2e6b2fe8c90e7c68d5f4c7559bffd88a3df166c1308
-size 13613
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_818.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_818.py
deleted file mode 100644
index 783accb582d2e23d64375c4c38d40f85942f6e37..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_818.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_813.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_814.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_815.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_816.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_817.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_818.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_818.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_818.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_818.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_819.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_819.py
deleted file mode 100644
index bf429e38b5d5a546a13461cd4db7ba4edacae167..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_819.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_807.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_810.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_811.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_812.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_818.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_819.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_819.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_819.py.debug_pkl
deleted file mode 100644
index 84b76ba1bd24de6a73227d11d8f535132fdd5793..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_819.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b119eee84a66d6deefe324b11ae10c57322b6b17e7ca6c3354e0e062cac34d01
-size 1495
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_823.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_823.py
deleted file mode 100644
index 82e0803af0d81b8930b52242603fa885377f9e95..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_823.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_821.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_822.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_823.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_823.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_823.py.debug_pkl
deleted file mode 100644
index cde4d29e8af8902de284d4559e8fd95f9224e95a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_823.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ca89f8d62f59a130c44b67295efb5c5eab64579a4202ebef275f913adf19268f
-size 13613
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_831.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_831.py
deleted file mode 100644
index 7a1b1c1b350eb8015f771dbc69ec4254a944b8c9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_831.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_826.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_827.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_828.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_829.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_830.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_831.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_831.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_831.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_831.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_832.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_832.py
deleted file mode 100644
index 288eb7ba9f0858c54b09d5762efdc1fef61adb59..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_832.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_820.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_823.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_824.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_825.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_831.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_832.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_832.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_832.py.debug_pkl
deleted file mode 100644
index 668fc3ed00f5ce3588bce1e8539ebe8ad81c575a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_832.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d837b55c02fa7a67d1d950e2b0a4ffb41f8885992ed4d9ee5dc8311e0d1567e0
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diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_836.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_836.py
deleted file mode 100644
index c22663dbcb67434b37888e7b9d5fe43aae8e2b19..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_836.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_834.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_835.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_836.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_836.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_836.py.debug_pkl
deleted file mode 100644
index d2eb354644d3e312641d1da06269ba472b4ac20b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_836.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4b0c080089c01572785443274c7ba01be5ef739474ea2fadb99aa2cb313c689e
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diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_844.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_844.py
deleted file mode 100644
index 92a4bc27387dab67deeeab6fdef3482b9ae8a881..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_844.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_839.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_840.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_841.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_842.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_843.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_844.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_844.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_844.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_844.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_845.py b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_845.py
deleted file mode 100644
index 665888b60941e0ea44e778513e539481a01f40c3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_845.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_833.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_836.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_837.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_838.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_844.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_845.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_845.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_845.py.debug_pkl
deleted file mode 100644
index aaf62817befb474da566d9cb6526a60a4bb0dfc6..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/fcu/___torch_mangle_845.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:07b580b06cf3b286a14adc2afc8448b87def7ec6a363c3a66376e24a82356695
-size 1495
diff --git a/xe_weights14/glonet_p1/code/__torch__/modelp2.py b/xe_weights14/glonet_p1/code/__torch__/modelp2.py
deleted file mode 100644
index c3557c182ebe2d9ad6f80fc864c64a8e9288ff08..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modelp2.py
+++ /dev/null
@@ -1,71 +0,0 @@
-class Glonet(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  jump : __torch__.modules1.residual
-  space : __torch__.sblock.FoTF
-  maps : __torch__.utils.Encoder
-  dynamics : __torch__.dblock.TeDev
-  mapsback : __torch__.utils.Decoder
-  def forward(self: __torch__.modelp2.Glonet,
-    input: Tensor) -> Tensor:
-    mapsback = self.mapsback
-    dynamics = self.dynamics
-    maps = self.maps
-    space = self.space
-    jump = self.jump
-    B = ops.prim.NumToTensor(torch.size(input, 0))
-    _0 = int(B)
-    _1 = int(B)
-    T = ops.prim.NumToTensor(torch.size(input, 1))
-    _2 = int(T)
-    _3 = int(T)
-    C = ops.prim.NumToTensor(torch.size(input, 2))
-    _4 = int(C)
-    _5 = int(C)
-    H = ops.prim.NumToTensor(torch.size(input, 3))
-    _6 = int(H)
-    _7 = int(H)
-    W = ops.prim.NumToTensor(torch.size(input, 4))
-    _8 = int(W)
-    _9 = int(W)
-    x_raw = torch.to(input, dtype=6, layout=0, device=torch.device("cuda:0"))
-    skip_feature = torch.contiguous((jump).forward(x_raw, ))
-    x_raw0 = torch.to(x_raw, dtype=6, layout=0, device=torch.device("cuda:0"))
-    spatial_feature = torch.contiguous((space).forward(x_raw0, ))
-    skip_feature0 = torch.detach(skip_feature)
-    spatial_feature0 = torch.detach(spatial_feature)
-    _10 = torch.reshape(spatial_feature0, [-1, _5, _7, _9])
-    spatial_feature1 = torch.contiguous(_10)
-    input0 = torch.to(spatial_feature1, dtype=6, layout=0, device=torch.device("cuda:0"))
-    _11, _12, = (maps).forward(input0, )
-    spatial_embed = torch.detach(_11)
-    spatial_skip_feature = torch.detach(_12)
-    spatial_embed0 = torch.contiguous(spatial_embed)
-    spatial_skip_feature0 = torch.contiguous(spatial_skip_feature)
-    C_ = ops.prim.NumToTensor(torch.size(spatial_embed0, 1))
-    _13 = int(C_)
-    _14 = int(C_)
-    H_ = ops.prim.NumToTensor(torch.size(spatial_embed0, 2))
-    _15 = int(H_)
-    _16 = int(H_)
-    W_ = ops.prim.NumToTensor(torch.size(spatial_embed0, 3))
-    _17 = int(W_)
-    _18 = torch.view(spatial_embed0, [_1, _3, _14, _16, int(W_)])
-    spatial_embed1 = torch.contiguous(_18)
-    x = torch.to(spatial_embed1, dtype=6, layout=0, device=torch.device("cuda:0"))
-    spatialtemporal_embed = torch.contiguous((dynamics).forward(x, ))
-    spatialtemporal_embed0 = torch.detach(spatialtemporal_embed)
-    _19 = [int(torch.mul(B, T)), _13, _15, _17]
-    _20 = torch.reshape(spatialtemporal_embed0, _19)
-    spatialtemporal_embed1 = torch.contiguous(_20)
-    input1 = torch.to(spatialtemporal_embed1, dtype=6, layout=0, device=torch.device("cuda:0"))
-    enc1 = torch.to(spatial_skip_feature0, dtype=6, layout=0, device=torch.device("cuda:0"))
-    predictions = torch.contiguous((mapsback).forward(input1, enc1, ))
-    predictions0 = torch.detach(predictions)
-    _21 = torch.reshape(predictions0, [_0, _2, _4, _6, _8])
-    _22 = torch.mul(torch.contiguous(_21), CONSTANTS.c0)
-    skip_feature1 = torch.to(skip_feature0, dtype=6, layout=0, device=torch.device("cuda:0"))
-    predictions1 = torch.add(_22, skip_feature1)
-    return torch.contiguous(predictions1)
diff --git a/xe_weights14/glonet_p1/code/__torch__/modelp2.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modelp2.py.debug_pkl
deleted file mode 100644
index 1416a4669372e757cd02b34246873efb0a374440..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modelp2.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ac627ef5338a1fdf76e34884e2e35a42872a03604e3ffdfdf3ae047cacb2aa71
-size 7253
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1.py b/xe_weights14/glonet_p1/code/__torch__/modules1.py
deleted file mode 100644
index 0253a936e5b3db7c4dd2ac068e45e0bcb1ce6164..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1.py
+++ /dev/null
@@ -1,222 +0,0 @@
-class residual(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  maps : __torch__.modules1.Encoder
-  mmb : __torch__.modules1.MMB
-  mapsback : __torch__.modules1.Decoder
-  def forward(self: __torch__.modules1.residual,
-    x_raw: Tensor) -> Tensor:
-    mapsback = self.mapsback
-    mmb = self.mmb
-    maps = self.maps
-    B = ops.prim.NumToTensor(torch.size(x_raw, 0))
-    _0 = int(B)
-    _1 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x_raw, 1))
-    _2 = int(T)
-    _3 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x_raw, 2))
-    _4 = int(C)
-    _5 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x_raw, 3))
-    _6 = int(H)
-    _7 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x_raw, 4))
-    _8 = int(W)
-    _9 = int(W)
-    input = torch.view(x_raw, [int(torch.mul(B, T)), _5, _7, _9])
-    _10, _11, = (maps).forward(input, )
-    C_ = ops.prim.NumToTensor(torch.size(_10, 1))
-    _12 = int(C_)
-    _13 = int(C_)
-    H_ = ops.prim.NumToTensor(torch.size(_10, 2))
-    _14 = int(H_)
-    _15 = int(H_)
-    W_ = ops.prim.NumToTensor(torch.size(_10, 3))
-    _16 = int(W_)
-    x = torch.view(_10, [_1, _3, _13, _15, int(W_)])
-    _17 = (mmb).forward(x, )
-    _18 = [int(torch.mul(B, T)), _12, _14, _16]
-    input0 = torch.reshape(_17, _18)
-    _19 = torch.reshape((mapsback).forward(input0, _11, ), [_0, _2, _4, _6, _8])
-    return _19
-class Encoder(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  enc : __torch__.torch.nn.modules.container.Sequential
-  def forward(self: __torch__.modules1.Encoder,
-    input: Tensor) -> Tuple[Tensor, Tensor]:
-    enc = self.enc
-    _3 = getattr(enc, "3")
-    enc0 = self.enc
-    _2 = getattr(enc0, "2")
-    enc1 = self.enc
-    _1 = getattr(enc1, "1")
-    enc2 = self.enc
-    _0 = getattr(enc2, "0")
-    _20 = (_0).forward(input, )
-    _21 = (_3).forward((_2).forward((_1).forward(_20, ), ), )
-    return (_21, _20)
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.BasicConv2d
-  def forward(self: __torch__.modules1.ConvSC,
-    input: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(input, )
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.GroupNorm
-  act : __torch__.torch.nn.modules.activation.LeakyReLU
-  def forward(self: __torch__.modules1.BasicConv2d,
-    input: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _22 = (norm).forward((conv).forward(input, ), )
-    return (act).forward(_22, )
-class MMB(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  enc : __torch__.torch.nn.modules.container.___torch_mangle_165.Sequential
-  dec : __torch__.torch.nn.modules.container.___torch_mangle_318.Sequential
-  def forward(self: __torch__.modules1.MMB,
-    x: Tensor) -> Tensor:
-    dec = self.dec
-    _7 = getattr(dec, "7")
-    dec0 = self.dec
-    _6 = getattr(dec0, "6")
-    dec1 = self.dec
-    _5 = getattr(dec1, "5")
-    dec2 = self.dec
-    _4 = getattr(dec2, "4")
-    dec3 = self.dec
-    _3 = getattr(dec3, "3")
-    dec4 = self.dec
-    _2 = getattr(dec4, "2")
-    dec5 = self.dec
-    _1 = getattr(dec5, "1")
-    dec6 = self.dec
-    _0 = getattr(dec6, "0")
-    enc = self.enc
-    _70 = getattr(enc, "7")
-    enc3 = self.enc
-    _60 = getattr(enc3, "6")
-    enc4 = self.enc
-    _50 = getattr(enc4, "5")
-    enc5 = self.enc
-    _40 = getattr(enc5, "4")
-    enc6 = self.enc
-    _30 = getattr(enc6, "3")
-    enc7 = self.enc
-    _20 = getattr(enc7, "2")
-    enc8 = self.enc
-    _10 = getattr(enc8, "1")
-    enc9 = self.enc
-    _00 = getattr(enc9, "0")
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _23 = int(B)
-    _24 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _25 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _26 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _27 = int(H)
-    _28 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _29 = int(W)
-    _8 = int(W)
-    _31 = [_24, int(torch.mul(T, C)), _28, _8]
-    input = torch.reshape(x, _31)
-    _32 = (_00).forward(input, )
-    _33 = (_10).forward(_32, )
-    _34 = (_20).forward(_33, )
-    _35 = (_30).forward(_34, )
-    _36 = (_40).forward(_35, )
-    _37 = (_50).forward(_36, )
-    _38 = (_60).forward(_37, )
-    _39 = (_0).forward((_70).forward(_38, ), )
-    input1 = torch.cat([_39, _38], 1)
-    input2 = torch.cat([(_1).forward(input1, ), _37], 1)
-    input3 = torch.cat([(_2).forward(input2, ), _36], 1)
-    input4 = torch.cat([(_3).forward(input3, ), _35], 1)
-    input5 = torch.cat([(_4).forward(input4, ), _34], 1)
-    input6 = torch.cat([(_5).forward(input5, ), _33], 1)
-    input7 = torch.cat([(_6).forward(input6, ), _32], 1)
-    hid = torch.reshape((_7).forward(input7, ), [_23, _25, _26, _27, _29])
-    return hid
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_15.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_31.Sequential
-  def forward(self: __torch__.modules1.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _40 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_40, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_40, ))
-    y1 = torch.add_(y0, (_2).forward(_40, ))
-    return torch.add_(y1, (_3).forward(_40, ))
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_16.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_17.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_18.LeakyReLU
-  def forward(self: __torch__.modules1.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _41 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_41, )
-class Decoder(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  dec : __torch__.torch.nn.modules.container.___torch_mangle_338.Sequential
-  readout : __torch__.torch.nn.modules.conv.___torch_mangle_339.Conv2d
-  def forward(self: __torch__.modules1.Decoder,
-    input: Tensor,
-    argument_2: Tensor) -> Tensor:
-    readout = self.readout
-    dec = self.dec
-    _3 = getattr(dec, "3")
-    dec7 = self.dec
-    _2 = getattr(dec7, "2")
-    dec8 = self.dec
-    _1 = getattr(dec8, "1")
-    dec9 = self.dec
-    _0 = getattr(dec9, "0")
-    _42 = (_1).forward((_0).forward(input, ), )
-    input8 = torch.cat([(_2).forward(_42, ), argument_2], 1)
-    _43 = (readout).forward((_3).forward(input8, ), )
-    return _43
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1.py.debug_pkl
deleted file mode 100644
index 37e6874f412e75fef907ccdb40217b3c9123c0f8..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f6421323e1583dc93881f914a989038aa94b95362a0450c27a25f237a55e902e
-size 7537
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_101.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_101.py
deleted file mode 100644
index f9a74041bab483dfe71b512f03d62ed0fcf75386..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_101.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_98.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_99.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_100.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_101.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_101.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_101.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_101.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_105.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_105.py
deleted file mode 100644
index 3601e011dc40daebd9d83fa3f501862a1531b445..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_105.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_102.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_103.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_104.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_105.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_105.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_105.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_105.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_107.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_107.py
deleted file mode 100644
index ec9589fe70863f9810dbfc9174a8df832c17d8a0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_107.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_89.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_106.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_107.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_107.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_107.py.debug_pkl
deleted file mode 100644
index 013131a1fea49d5a20b7971ae934c57cb1fb745a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_107.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d08edf335edcf001849d8a973baa100618e05e108c92e165f36854230851ec28
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_112.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_112.py
deleted file mode 100644
index 045c34f5930bcadffff47039b92e1fd07f3abf54..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_112.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_109.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_110.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_111.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_112.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_112.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_112.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_112.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_116.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_116.py
deleted file mode 100644
index a4410c0312526fd159891f3879feb436a36af77c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_116.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_113.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_114.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_115.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_116.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_116.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_116.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_116.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_120.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_120.py
deleted file mode 100644
index 14e59d5ea63a5ec923d146d22a44fad132a83cec..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_120.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_117.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_118.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_119.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_120.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_120.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_120.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_120.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_124.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_124.py
deleted file mode 100644
index 8abb6802f44aa021056e2f53e90ea08b30c363f0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_124.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_121.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_122.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_123.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_124.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_124.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_124.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_124.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_126.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_126.py
deleted file mode 100644
index c8c40d24db8db81b213bf0b3785ed305aa681273..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_126.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_108.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_125.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_126.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_126.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_126.py.debug_pkl
deleted file mode 100644
index cc2d69c45b7a3262bd5b0a7c261613fd423e0075..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_126.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_13.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_13.py
deleted file mode 100644
index 064575a7f51c8add32bb5447ac447876a1cb378e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_13.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_10.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_11.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_12.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_13.BasicConv2d,
-    argument_1: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_13.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_13.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_13.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_131.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_131.py
deleted file mode 100644
index 518b47eff50c07ab7f644e68c95d92320e9c992b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_131.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_128.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_129.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_130.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_131.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_131.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_131.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_131.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_135.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_135.py
deleted file mode 100644
index 1e49a731aac1fe53cfe3fb31b3c49d12feb604d9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_135.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_132.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_133.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_134.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_135.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_135.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_135.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_135.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_139.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_139.py
deleted file mode 100644
index 2b0089046aa10beb64dabea83d3692f57cd7381e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_139.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_136.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_137.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_138.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_139.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_139.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_139.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_139.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_14.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_14.py
deleted file mode 100644
index 58e0e7b8ca680701c9bfc7a826397fd73f6c195a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_14.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_13.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_14.ConvSC,
-    argument_1: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(argument_1, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_14.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_14.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_14.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_143.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_143.py
deleted file mode 100644
index 698d939739721f3790ea914e70de543e2bf04a98..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_143.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_140.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_141.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_142.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_143.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_143.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_143.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_143.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_145.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_145.py
deleted file mode 100644
index 0a37ecead870dc33645db34e215c419eae64fd68..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_145.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_127.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_144.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_145.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_145.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_145.py.debug_pkl
deleted file mode 100644
index c0b43c3d8fff2c07ae391cf33c26867b40f84419..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_145.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_150.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_150.py
deleted file mode 100644
index dd8aa7cf1be9261e6a87caa797c3ed3f70171bbe..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_150.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_147.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_148.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_149.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_150.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_150.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_150.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_150.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_154.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_154.py
deleted file mode 100644
index 272b4264d3bd3dea37b08581b6e999fd38ee09f2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_154.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_151.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_152.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_153.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_154.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_154.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_154.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_154.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_158.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_158.py
deleted file mode 100644
index 4dab7d854d992394ada0784366de5b520c3ebc9b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_158.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_155.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_156.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_157.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_158.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_158.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_158.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_158.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_162.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_162.py
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index 3a0d823f1576df444a778d6935cf1eed5d088eed..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_162.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_159.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_160.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_161.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_162.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_162.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_162.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_162.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_164.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_164.py
deleted file mode 100644
index 6ba1d16ff09ccabd1114829e516bed268a6dedec..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_164.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_146.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_163.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_164.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_164.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_164.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_164.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_170.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_170.py
deleted file mode 100644
index d56d33dd83c49e39d1c8eceb3663325cde990fcb..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_170.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_167.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_168.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_169.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_170.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_170.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_170.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_170.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_174.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_174.py
deleted file mode 100644
index 5aa4b48ef95dbd0058b73b6d28de792aa3dbfb9a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_174.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_171.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_172.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_173.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_174.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_174.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_174.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_174.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_178.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_178.py
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index 1dfa1eae8acfc0a33e41b729e4478d75d31dc07c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_178.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_175.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_176.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_177.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_178.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_178.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_178.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_182.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_182.py
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index 3ca80905b52b486b4b51513488830889ba33004c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_182.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_179.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_180.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_181.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_182.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_182.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_182.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_184.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_184.py
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_184.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_166.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_183.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_184.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
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-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_184.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_184.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_189.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_186.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_187.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_188.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_189.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_189.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_189.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_189.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_193.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_193.py
deleted file mode 100644
index 27f50ba5eac3b4c3aafd7414120ee20819c243e0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_193.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_190.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_191.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_192.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_193.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_193.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_193.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_193.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_197.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_197.py
deleted file mode 100644
index 8b5d782cd0a887423ecb3212861e106bf4b1a8e4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_197.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_194.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_195.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_196.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_197.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_197.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_197.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_197.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_201.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_201.py
deleted file mode 100644
index 0fe8376b95a9b76d1ed68593ede48e39d9bbe3d4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_201.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_198.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_199.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_200.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_201.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_201.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_201.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_201.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_203.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_203.py
deleted file mode 100644
index 97ba5f416b3bc47cdda06fa2c91d3f15c5a75ea4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_203.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_185.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_202.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_203.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_203.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_203.py.debug_pkl
deleted file mode 100644
index f6160c1c7093ed93f2b8963410bbc7dfa146aa25..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_203.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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-size 1890
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_208.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_208.py
deleted file mode 100644
index 5f3576d4f0499629cb1887c385fea5ce1eec495f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_208.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_205.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_206.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_207.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_208.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_208.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_208.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_208.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_212.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_212.py
deleted file mode 100644
index a0d3136751f2bd71fe0d42d26a80c2c5bbc7d4ce..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_212.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_209.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_210.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_211.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_212.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_212.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_212.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_212.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_216.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_216.py
deleted file mode 100644
index 288cbc5c962c6d8984367ff15eb2d68468d6520b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_216.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_213.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_214.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_215.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_216.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_216.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_216.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_216.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_22.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_22.py
deleted file mode 100644
index ccd37a44494ff0ee6e524541b428ceb54be1c1d9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_22.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_19.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_20.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_21.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_22.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_22.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_22.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_22.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_220.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_220.py
deleted file mode 100644
index d86a5e3c8578b29e9a03886f02a33d8767be1701..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_220.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_217.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_218.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_219.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_220.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_220.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_220.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_220.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_222.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_222.py
deleted file mode 100644
index c471b1e09c8557dfaf689f5ab996f4d0cf5fba33..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_222.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_204.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_221.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_222.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_222.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_222.py.debug_pkl
deleted file mode 100644
index 983f3e07530358fa0b779914e5ef9e99ee6f69ef..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_222.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_227.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_227.py
deleted file mode 100644
index 473f29ff30984002139b3dd6571cdbeadf9bbdfe..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_227.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_224.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_225.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_226.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_227.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_227.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_227.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_227.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_231.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_231.py
deleted file mode 100644
index 8bcf3690c09edb8e909b0812fedf35cef328d348..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_231.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_228.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_229.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_230.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_231.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_231.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_231.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_231.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_235.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_235.py
deleted file mode 100644
index 3e75d8f05cc010ac1960c0ece10f9840bb061a28..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_235.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_232.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_233.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_234.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_235.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_235.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_235.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_235.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_239.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_239.py
deleted file mode 100644
index cca638271d6ff60b320a3e7e5f1546a23ffa29e3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_239.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_236.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_237.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_238.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_239.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_239.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_239.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_239.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_241.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_241.py
deleted file mode 100644
index 842854ff37d8c71a0bbf8040323c3197e2eaf9fc..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_241.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_223.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_240.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_241.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_241.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_241.py.debug_pkl
deleted file mode 100644
index 93cc0f5a28f5fea3188c0ce93fc537b73048ed58..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_241.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_246.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_246.py
deleted file mode 100644
index e7fd8cb4a952e44b9320abcb8d9fec145872ebcd..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_246.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_243.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_244.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_245.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_246.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_246.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_246.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_246.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_250.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_250.py
deleted file mode 100644
index 1156b53d651b031c2fe92ae82984ceba9b525396..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_250.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_247.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_248.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_249.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_250.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_250.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_250.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_250.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_254.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_254.py
deleted file mode 100644
index 2cb2fecb3218be19bf1f641066d76b904641c5a5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_254.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_251.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_252.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_253.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_254.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_254.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_254.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_254.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_258.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_258.py
deleted file mode 100644
index ac175524a5759a7a70f595be15ce1ada11b8189e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_258.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_255.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_256.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_257.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_258.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_258.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_258.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_258.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_26.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_26.py
deleted file mode 100644
index 3abdac87068ff67ba13a4d262f53bd92cfd73de4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_26.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_23.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_24.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_25.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_26.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_26.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_26.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_26.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_260.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_260.py
deleted file mode 100644
index 21115b74a3cafbe83614b2a3b08147edf3ae1aa2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_260.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_242.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_259.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_260.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_260.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_260.py.debug_pkl
deleted file mode 100644
index 5b3d158e4f21debef44c18b50011732c25db5365..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_260.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_265.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_265.py
deleted file mode 100644
index 991b334cb1f77cbaf128df235f41c001d91c2fed..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_265.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_262.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_263.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_264.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_265.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_265.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_265.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_265.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_269.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_269.py
deleted file mode 100644
index 335c4627bc67f03bf85cd459b5aa9fa620723f52..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_269.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_266.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_267.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_268.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_269.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_269.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_269.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_269.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_273.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_273.py
deleted file mode 100644
index dbe896637e15f3ba2eae5834baebaa6b58e5151f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_273.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_270.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_271.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_272.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_273.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_273.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_273.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_273.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_277.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_277.py
deleted file mode 100644
index 73c383f84086c390fe54578e76b2e3234e48c0cd..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_277.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_274.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_275.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_276.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_277.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_277.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_277.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_277.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_279.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_279.py
deleted file mode 100644
index b3ec377474b06574f47aac871804128acceec749..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_279.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_261.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_278.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_279.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_279.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_279.py.debug_pkl
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index b508af27634e70c72abd44901676caf7e057c554..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_279.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_284.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_284.py
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index 8e891aed554d8e6169a0b5c254b5a7847e9cbe26..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_284.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_281.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_282.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_283.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_284.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_284.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_284.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_284.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_288.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_288.py
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index 5581a37dffb35d99a20d8e6d84a88abae32b1bb2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_288.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_285.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_286.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_287.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_288.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_288.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_288.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_288.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_292.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_292.py
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index 0a83bdd032a7f8cf4a6433e670184699689855a1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_292.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_289.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_290.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_291.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_292.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_292.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_292.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_292.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_296.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_296.py
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index fdc07c9a6efd83e5ae2ebf0a190243d845dd09b1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_296.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_293.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_294.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_295.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_296.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_296.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_296.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_296.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_298.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_298.py
deleted file mode 100644
index 66beca1261889e9c74c6e2dfb806c43388fbeebb..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_298.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_280.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_297.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_298.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_298.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_298.py.debug_pkl
deleted file mode 100644
index a0378fb0c095347ea060313bf647d106e13134ae..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_298.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_3.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_3.py
deleted file mode 100644
index 6bc3334e0ca9d63087543b1bc9430ff113c06abe..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_3.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_0.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_1.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_2.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_3.BasicConv2d,
-    argument_1: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_3.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_3.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_3.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_30.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_30.py
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index f8d979f7756c3d5ceb5bf835ef82c774a49a3d10..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_30.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_27.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_28.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_29.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_30.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_30.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_30.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_30.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_303.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_303.py
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index 297c75f8104ed34caf4fa7cb3e3195d3400ba529..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_303.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_300.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_301.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_302.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_303.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_303.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_303.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_303.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_307.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_307.py
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index 489246f0c8c5f52b03f76015ebcf34c79b2087e5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_307.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_304.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_305.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_306.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_307.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_307.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_307.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_307.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_311.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_311.py
deleted file mode 100644
index ee59d9ee6a7707212b39af335c7a31c51e5072c8..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_311.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_308.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_309.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_310.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_311.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_311.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_311.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_311.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_315.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_315.py
deleted file mode 100644
index 5f6508b1402985003103c183420b1cb8c43d6d86..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_315.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_312.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_313.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_314.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_315.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_315.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_315.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_315.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_317.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_317.py
deleted file mode 100644
index 3e924d467ac042f8518a8c0fc0c762123946a953..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_317.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_299.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_316.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_317.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_317.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_317.py.debug_pkl
deleted file mode 100644
index 5f02008dae541111d10fd95ad4574b1ec769ac77..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_317.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_321.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_321.py
deleted file mode 100644
index 14f6ee47df8fe7509d7518ee9c60872fcae33e55..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_321.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.ConvTranspose2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_319.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_320.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_321.BasicConv2d,
-    input: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(input, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_321.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_321.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_321.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_322.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_322.py
deleted file mode 100644
index 2287c6e69e49765c24b8fab9e2ecdb32edbc04c3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_322.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_321.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_322.ConvSC,
-    input: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(input, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_322.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_322.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_322.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_326.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_326.py
deleted file mode 100644
index 7360d1f5e5c7ce86597d8e9fd84e6087fc4d63e5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_326.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_323.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_324.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_325.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_326.BasicConv2d,
-    argument_1: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_326.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_326.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_326.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_327.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_327.py
deleted file mode 100644
index 8e457b1783c2c4d11979a4fedb23e28b16a88e21..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_327.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_326.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_327.ConvSC,
-    argument_1: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(argument_1, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_327.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_327.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_327.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_331.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_331.py
deleted file mode 100644
index 7fb5bd11ca79ede58dcf502088dc1055ea9bf3b0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_331.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_328.ConvTranspose2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_329.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_330.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_331.BasicConv2d,
-    argument_1: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_331.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_331.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_331.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_332.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_332.py
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index bc366373b54165ad6d9045a5b0f82d1de2b8e462..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_332.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_331.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_332.ConvSC,
-    argument_1: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(argument_1, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_332.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_332.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_332.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_336.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_336.py
deleted file mode 100644
index 200b3a69fe74b4fe41a6d14591334a91b8235b98..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_336.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_333.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_334.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_335.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_336.BasicConv2d,
-    input: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(input, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_336.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_336.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_336.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_337.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_337.py
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index bce89e18ade0fc09cf1b562e8cbf74ffdb944ee7..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_337.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_336.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_337.ConvSC,
-    input: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(input, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_337.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_337.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_337.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_36.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_36.py
deleted file mode 100644
index 10c34c7a7f6369fd3f99c6d327662472643bcfb0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_36.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_33.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_34.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_35.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_36.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_36.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_36.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_36.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_4.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_4.py
deleted file mode 100644
index 9fd0b4d1504046d65339d8e18be9189b99080304..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_4.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_3.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_4.ConvSC,
-    argument_1: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(argument_1, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_4.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_4.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_4.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_40.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_40.py
deleted file mode 100644
index a8aac2c1d7bc5b63f17f7a12355159994c7f5e22..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_40.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_37.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_38.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_39.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_40.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_40.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_40.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_40.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_44.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_44.py
deleted file mode 100644
index fe9773f8c93e2451d9d3cebc303cddb47b6b6ea2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_44.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_41.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_42.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_43.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_44.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_44.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_44.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_44.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_48.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_48.py
deleted file mode 100644
index 9dfbbdc31ccbcfde75395692c2ad026a0ba2fb7d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_48.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_45.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_46.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_47.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_48.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_48.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_48.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_48.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_50.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_50.py
deleted file mode 100644
index ba7b1ccbc0b2b8fff115bd3bc509a872da93752d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_50.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_32.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_49.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_50.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_50.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_50.py.debug_pkl
deleted file mode 100644
index 28a00bc9e65aa6d20d18468122ae2dfe2adf24cb..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_50.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_55.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_55.py
deleted file mode 100644
index 512fbfd10bf28e8cc95c06ee5a9f72d863048ea9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_55.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_52.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_53.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_54.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_55.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_55.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_55.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_55.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_59.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_59.py
deleted file mode 100644
index e9b399310d1dbc67074ba45823c358459c506c9d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_59.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_56.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_57.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_58.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_59.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_59.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_59.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_59.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_63.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_63.py
deleted file mode 100644
index 675d8585333b4647332e1f5af630729b0dd9419b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_63.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_60.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_61.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_62.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_63.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_63.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_63.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_63.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_67.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_67.py
deleted file mode 100644
index 32c6ff38bc16d606aeb7c8b31f83871d4723f4ec..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_67.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_64.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_65.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_66.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_67.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_67.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_67.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_67.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_69.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_69.py
deleted file mode 100644
index cd0727b173cf797925450a629d4a1b3a89d83183..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_69.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_51.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_68.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_69.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_69.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_69.py.debug_pkl
deleted file mode 100644
index 34d342974c2615a496d000c0701d33e5e8425a93..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_69.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_74.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_74.py
deleted file mode 100644
index 00276b44c82a8c1c2573e2845d189ee2c523ee90..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_74.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_71.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_72.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_73.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_74.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_74.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_74.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_74.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_78.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_78.py
deleted file mode 100644
index c384b4f149c93286bacae968a04e5f35b905baec..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_78.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_75.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_76.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_77.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_78.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_78.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_78.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_78.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_8.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_8.py
deleted file mode 100644
index 00185510f0e8482046c4576bbf4c791728b1a8b9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_8.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_5.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_6.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_7.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_8.BasicConv2d,
-    argument_1: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_8.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_8.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_8.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_82.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_82.py
deleted file mode 100644
index 976456dc0867d1a4d07bb9df72f712d7b7f6d21d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_82.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_79.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_80.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_81.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_82.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_82.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_82.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_82.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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-size 106
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_86.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_86.py
deleted file mode 100644
index e7c230a56d3f87a5f32cf616fb8e46ff1b3400b4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_86.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_83.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_84.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_85.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_86.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_86.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_86.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_86.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_88.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_88.py
deleted file mode 100644
index b1ae7973553d0e01af3b1461a923850bab494924..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_88.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_70.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_87.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_88.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_88.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_88.py.debug_pkl
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index 5aa26d2e04bfa57b00f546bd0b46a6bacc44a5e0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_88.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:98545e752ddc5d24428c2003b464169515860e6e3dc5624d900bde15029be382
-size 1890
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_9.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_9.py
deleted file mode 100644
index 5a53ffe2f99d9028ca2a22f47e152cde5ca5a71f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_9.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_8.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_9.ConvSC,
-    argument_1: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(argument_1, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_9.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_9.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_9.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_93.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_93.py
deleted file mode 100644
index 1c3d286d216a35b6cd22e3058e70c93177b5fab5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_93.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_90.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_91.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_92.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_93.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_93.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_93.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_93.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_97.py b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_97.py
deleted file mode 100644
index 6c883d28565e3f8be6742020e622cb9d0870d706..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_97.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_94.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_95.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_96.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_97.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_97.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_97.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/modules1/___torch_mangle_97.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/sblock.py b/xe_weights14/glonet_p1/code/__torch__/sblock.py
deleted file mode 100644
index d652501e497dc7f2ff9fb22c85ea6d27f2ac5ec1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/sblock.py
+++ /dev/null
@@ -1,174 +0,0 @@
-class FoTF(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  NN : __torch__.NN.Local_CNN_Branch
-  NO : __torch__.NO.GF_Block
-  up : __torch__.torch.nn.modules.conv.___torch_mangle_529.ConvTranspose2d
-  down : __torch__.torch.nn.modules.conv.___torch_mangle_530.Conv2d
-  conv1x1 : __torch__.torch.nn.modules.conv.___torch_mangle_531.Conv2d
-  def forward(self: __torch__.sblock.FoTF,
-    x_raw: Tensor) -> Tensor:
-    down = self.down
-    conv1x1 = self.conv1x1
-    up = self.up
-    NN = self.NN
-    NO = self.NO
-    B = ops.prim.NumToTensor(torch.size(x_raw, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    _4 = int(B)
-    _5 = int(B)
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-    _7 = int(B)
-    _8 = int(B)
-    _9 = int(B)
-    _10 = int(B)
-    _11 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x_raw, 1))
-    _12 = int(T)
-    _13 = int(T)
-    _14 = int(T)
-    _15 = int(T)
-    _16 = int(T)
-    _17 = int(T)
-    _18 = int(T)
-    _19 = int(T)
-    _20 = int(T)
-    _21 = int(T)
-    _22 = int(T)
-    _23 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x_raw, 2))
-    _24 = int(C)
-    _25 = int(C)
-    _26 = int(C)
-    _27 = int(C)
-    _28 = int(C)
-    _29 = int(C)
-    _30 = int(C)
-    _31 = int(C)
-    _32 = int(C)
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-    _34 = int(C)
-    _35 = int(C)
-    _36 = int(C)
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-    _40 = int(C)
-    _41 = int(C)
-    _42 = int(C)
-    _43 = int(C)
-    _44 = int(C)
-    _45 = int(C)
-    _46 = int(C)
-    _47 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x_raw, 3))
-    _48 = int(H)
-    _49 = int(H)
-    _50 = int(H)
-    _51 = int(H)
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-    _71 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x_raw, 4))
-    _72 = int(W)
-    _73 = int(W)
-    _74 = int(W)
-    _75 = int(W)
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-    _92 = int(W)
-    _93 = int(W)
-    _94 = int(W)
-    _95 = int(W)
-    _96 = (NO).forward(x_raw, )
-    _97 = (NN).forward(x_raw, )
-    _98 = [int(torch.mul(B, T)), _47, _71, _95]
-    input = torch.view(_96, _98)
-    gf_features_up = torch.view((up).forward(input, ), [_11, _23, _46, _70, _94])
-    _99 = [int(torch.mul(B, T)), _45, _69, _93]
-    input0 = torch.view(_97, _99)
-    lc_features = torch.view((conv1x1).forward(input0, ), [_10, _22, _44, _68, _92])
-    x = torch.add(gf_features_up, lc_features)
-    _100 = (NO).forward1(x, )
-    _101 = (NN).forward1(x, )
-    _102 = [int(torch.mul(B, T)), _43, _67, _91]
-    input1 = torch.view(_100, _102)
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-    _103 = [int(torch.mul(B, T)), _41, _65, _89]
-    input2 = torch.view(_101, _103)
-    lc_features0 = torch.view((conv1x1).forward1(input2, ), [_8, _20, _40, _64, _88])
-    x0 = torch.add(gf_features_down, lc_features0)
-    _104 = (NO).forward2(x0, )
-    _105 = (NN).forward2(x0, )
-    _106 = [int(torch.mul(B, T)), _39, _63, _87]
-    input3 = torch.view(_104, _106)
-    gf_features_up0 = torch.view((up).forward1(input3, ), [_7, _19, _38, _62, _86])
-    _107 = [int(torch.mul(B, T)), _37, _61, _85]
-    input4 = torch.view(_105, _107)
-    lc_features1 = torch.view((conv1x1).forward2(input4, ), [_6, _18, _36, _60, _84])
-    x1 = torch.add(gf_features_up0, lc_features1)
-    _108 = (NO).forward3(x1, )
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-    _110 = [int(torch.mul(B, T)), _35, _59, _83]
-    input5 = torch.view(_108, _110)
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-    _111 = [int(torch.mul(B, T)), _33, _57, _81]
-    input6 = torch.view(_109, _111)
-    lc_features2 = torch.view((conv1x1).forward3(input6, ), [_4, _16, _32, _56, _80])
-    x2 = torch.add(gf_features_down0, lc_features2)
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-    input7 = torch.view(_112, _114)
-    gf_features_up1 = torch.view((up).forward2(input7, ), [_3, _15, _30, _54, _78])
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-    input8 = torch.view(_113, _115)
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-    x3 = torch.add(gf_features_up1, lc_features3)
-    _116 = (NO).forward5(x3, )
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-    _118 = [int(torch.mul(B, T)), _27, _51, _75]
-    input9 = torch.view(_116, _118)
-    gf_features_down1 = torch.view((down).forward2(input9, ), [_1, _13, _26, _50, _74])
-    _119 = [int(torch.mul(B, T)), _25, _49, _73]
-    input10 = torch.view(_117, _119)
-    lc_features4 = torch.view((conv1x1).forward5(input10, ), [_0, _12, _24, _48, _72])
-    x4 = torch.add(gf_features_down1, lc_features4)
-    _120 = torch.add((NO).forward6(x4, ), (NN).forward6(x4, ))
-    return _120
diff --git a/xe_weights14/glonet_p1/code/__torch__/sblock.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/sblock.py.debug_pkl
deleted file mode 100644
index aec9408d17462f2b5d1ca878edf5af98c58961c2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/sblock.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation.py
deleted file mode 100644
index 80aaecfceef2cc9741ccc2fe1317fa7b0a60de6d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation.py
+++ /dev/null
@@ -1,74 +0,0 @@
-class LeakyReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    input = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return input
-class ReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-class MultiheadAttention(Module):
-  __parameters__ = ["in_proj_weight", "in_proj_bias", ]
-  __buffers__ = []
-  in_proj_weight : Tensor
-  in_proj_bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  out_proj : __torch__.torch.nn.modules.linear.NonDynamicallyQuantizableLinear
-class GELU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward2(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward4(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward6(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-class Tanh(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward2(self: __torch__.torch.nn.modules.activation.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward4(self: __torch__.torch.nn.modules.activation.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward6(self: __torch__.torch.nn.modules.activation.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_100.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_100.py
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_100.py
+++ /dev/null
@@ -1,9 +0,0 @@
-class LeakyReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_100.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_100.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_100.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_100.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_1002.py
+++ /dev/null
@@ -1,9 +0,0 @@
-class LeakyReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_1002.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    hid = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return hid
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_1002.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_1002.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_1007.py
+++ /dev/null
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-class LeakyReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_1007.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    input = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_1007.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_1007.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_104.py
+++ /dev/null
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-class LeakyReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_104.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_104.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_104.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_111.py
+++ /dev/null
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-class LeakyReLU(Module):
-  __parameters__ = []
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_111.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_111.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_111.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_115.py
+++ /dev/null
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-class LeakyReLU(Module):
-  __parameters__ = []
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_115.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_115.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_115.py.debug_pkl
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-class LeakyReLU(Module):
-  __parameters__ = []
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_119.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_119.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_119.py.debug_pkl
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_12.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    embed = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return embed
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_12.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_12.py.debug_pkl
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-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_123.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_123.py.debug_pkl
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-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_130.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_130.py.debug_pkl
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-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_2.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    input = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return input
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-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_314.py
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-class LeakyReLU(Module):
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_314.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
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-class LeakyReLU(Module):
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-    return input
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-class MultiheadAttention(Module):
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_356.GELU,
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_372.GELU,
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_384.py
+++ /dev/null
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-class MultiheadAttention(Module):
-  __parameters__ = ["in_proj_weight", "in_proj_bias", ]
-  __buffers__ = []
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-  in_proj_bias : Tensor
-  training : bool
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-  out_proj : __torch__.torch.nn.modules.linear.___torch_mangle_383.NonDynamicallyQuantizableLinear
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_388.py
+++ /dev/null
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-class GELU(Module):
-  __parameters__ = []
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward2(self: __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward4(self: __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_388.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_388.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_39.py
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-class LeakyReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_39.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
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-class ReLU(Module):
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
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+++ /dev/null
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-class MultiheadAttention(Module):
-  __parameters__ = ["in_proj_weight", "in_proj_bias", ]
-  __buffers__ = []
-  in_proj_weight : Tensor
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-  training : bool
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+++ /dev/null
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-class GELU(Module):
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_404.GELU,
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-    argument_1: Tensor) -> Tensor:
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-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_404.GELU,
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_404.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
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-class MultiheadAttention(Module):
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-class GELU(Module):
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-  def forward1(self: __torch__.torch.nn.modules.activation.___torch_mangle_420.GELU,
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-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_420.GELU,
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-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_420.GELU,
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_420.GELU,
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-    return torch.gelu(argument_1)
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-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
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-class MultiheadAttention(Module):
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_436.GELU,
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-class ReLU(Module):
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-class MultiheadAttention(Module):
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_468.GELU,
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-    return _0
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-  _is_full_backward_hook : Optional[bool]
-  out_proj : __torch__.torch.nn.modules.linear.___torch_mangle_495.NonDynamicallyQuantizableLinear
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_496.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_496.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_500.py
+++ /dev/null
@@ -1,26 +0,0 @@
-class GELU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward2(self: __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward4(self: __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_500.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_500.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_507.py
+++ /dev/null
@@ -1,5 +0,0 @@
-class ReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_507.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_507.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_512.py
+++ /dev/null
@@ -1,8 +0,0 @@
-class MultiheadAttention(Module):
-  __parameters__ = ["in_proj_weight", "in_proj_bias", ]
-  __buffers__ = []
-  in_proj_weight : Tensor
-  in_proj_bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  out_proj : __torch__.torch.nn.modules.linear.___torch_mangle_511.NonDynamicallyQuantizableLinear
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_512.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_512.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_516.py
+++ /dev/null
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-class GELU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward2(self: __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward4(self: __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_516.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_516.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_525.py
+++ /dev/null
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-class Tanh(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward2(self: __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward4(self: __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_525.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_525.py.debug_pkl
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+++ /dev/null
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-class LeakyReLU(Module):
-  __parameters__ = []
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_534.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    input = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return input
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_537.py
+++ /dev/null
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-class LeakyReLU(Module):
-  __parameters__ = []
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_537.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    spatial_embed = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return spatial_embed
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_537.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_537.py.debug_pkl
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-class LeakyReLU(Module):
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_54.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_54.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_54.py.debug_pkl
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_545.LeakyReLU,
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-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_545.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/activation/___torch_mangle_545.py.debug_pkl
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-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_552.LeakyReLU,
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-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
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-class LeakyReLU(Module):
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-    argument_1: Tensor) -> Tensor:
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index e29d404e4f757ebe0d7e792ed5533c16b2adaae0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/container/___torch_mangle_316.py
+++ /dev/null
@@ -1,9 +0,0 @@
-class Sequential(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  __annotations__["0"] = __torch__.modules1.___torch_mangle_303.GroupConv2d
-  __annotations__["1"] = __torch__.modules1.___torch_mangle_307.GroupConv2d
-  __annotations__["2"] = __torch__.modules1.___torch_mangle_311.GroupConv2d
-  __annotations__["3"] = __torch__.modules1.___torch_mangle_315.GroupConv2d
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/container/___torch_mangle_318.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Sequential(Module):
-  __parameters__ = []
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-  training : bool
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-  __annotations__["0"] = __torch__.modules1.___torch_mangle_184.Inception
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/container/___torch_mangle_338.py
+++ /dev/null
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-class Sequential(Module):
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/container/___torch_mangle_49.py
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-class Sequential(Module):
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-  __annotations__["0"] = __torch__.modules1.___torch_mangle_36.GroupConv2d
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/container/___torch_mangle_527.py
+++ /dev/null
@@ -1,80 +0,0 @@
-class Sequential(Module):
-  __parameters__ = []
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-  training : bool
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-  transposeconv1 : __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d
-  act1 : __torch__.torch.nn.modules.activation.Tanh
-  transposeconv2 : __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d
-  act2 : __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh
-  transposeconv3 : __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d
-  def forward(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
-    input: Tensor) -> Tensor:
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-    transposeconv1 = self.transposeconv1
-    _0 = (act1).forward((transposeconv1).forward(input, ), )
-    _1 = (act2).forward((transposeconv2).forward(_0, ), )
-    return (transposeconv3).forward(_1, )
-  def forward1(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
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-    transposeconv1 = self.transposeconv1
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-    _3 = (act2).forward1((transposeconv2).forward1(_2, ), )
-    return (transposeconv3).forward1(_3, )
-  def forward2(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
-    input: Tensor) -> Tensor:
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-    transposeconv1 = self.transposeconv1
-    _4 = (act1).forward2((transposeconv1).forward2(input, ), )
-    _5 = (act2).forward2((transposeconv2).forward2(_4, ), )
-    return (transposeconv3).forward2(_5, )
-  def forward3(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
-    input: Tensor) -> Tensor:
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-    _6 = (act1).forward3((transposeconv1).forward3(input, ), )
-    _7 = (act2).forward3((transposeconv2).forward3(_6, ), )
-    return (transposeconv3).forward3(_7, )
-  def forward4(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
-    input: Tensor) -> Tensor:
-    transposeconv3 = self.transposeconv3
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-    act1 = self.act1
-    transposeconv1 = self.transposeconv1
-    _8 = (act1).forward4((transposeconv1).forward4(input, ), )
-    _9 = (act2).forward4((transposeconv2).forward4(_8, ), )
-    return (transposeconv3).forward4(_9, )
-  def forward5(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
-    input: Tensor) -> Tensor:
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-    act1 = self.act1
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-    _10 = (act1).forward5((transposeconv1).forward5(input, ), )
-    _11 = (act2).forward5((transposeconv2).forward5(_10, ), )
-    return (transposeconv3).forward5(_11, )
-  def forward6(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
-    input: Tensor) -> Tensor:
-    transposeconv3 = self.transposeconv3
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-    _12 = (act1).forward6((transposeconv1).forward6(input, ), )
-    _13 = (act2).forward6((transposeconv2).forward6(_12, ), )
-    return (transposeconv3).forward6(_13, )
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/container/___torch_mangle_527.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/container/___torch_mangle_527.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/container/___torch_mangle_527.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/container/___torch_mangle_540.py
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-class Sequential(Module):
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/container/___torch_mangle_959.py
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-class Sequential(Module):
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-  __annotations__["0"] = __torch__.dblock.___torch_mangle_946.GroupConv2d
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/container/___torch_mangle_978.py
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-class Sequential(Module):
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-  __annotations__["0"] = __torch__.dblock.___torch_mangle_965.GroupConv2d
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/container/___torch_mangle_997.py
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-class Sequential(Module):
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-class Sequential(Module):
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index 8f9b72995925d7eb5f97de1ba0d7859231a0c21f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv.py
+++ /dev/null
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-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    input0 = torch._convolution(input, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input0
-class ConvTranspose2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input1 = torch._convolution(input, weight, bias, [2, 2], [1, 1], [1, 1], True, [1, 1], 1, False, False, True, True)
-    return input1
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
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-class Conv2d(Module):
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-    return input
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-    return input
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-class ConvTranspose2d(Module):
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-    return input0
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-    return input0
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-    return _0
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_108.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_108.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_108.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_108.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_108.py.debug_pkl
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index bf1bf11aea0c247abc275c69833ce9ee8c97670c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_109.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_109.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_109.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_109.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_113.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [2, 2], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_113.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_113.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_117.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-    argument_1: Tensor) -> Tensor:
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_117.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_117.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_121.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [5, 5], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_121.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_121.py.debug_pkl
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+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_127.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_127.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_128.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-    argument_1: Tensor) -> Tensor:
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_128.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_128.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_132.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_132.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_132.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_136.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_136.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_136.py.debug_pkl
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_140.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_140.py.debug_pkl
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_146.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_146.py.debug_pkl
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_147.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_147.py.debug_pkl
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-class Conv2d(Module):
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-    return input0
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_15.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_15.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_151.py
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-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_151.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [2, 2], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_151.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_151.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_155.py
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-class Conv2d(Module):
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-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_155.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [3, 3], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_155.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_155.py.debug_pkl
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-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_159.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_159.py.debug_pkl
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-class Conv2d(Module):
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-    return input
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-class Conv2d(Module):
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-    return input
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-class Conv2d(Module):
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-    return input
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_171.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_171.py.debug_pkl
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_175.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_175.py.debug_pkl
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-    return input
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-    return input0
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-    return input
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [2, 2], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_190.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_190.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_194.py
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@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_194.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_194.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_198.py
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_198.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_198.py.debug_pkl
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-    return input0
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-    return input
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-    return input
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-class Conv2d(Module):
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-    return input
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-    return input
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-    return input0
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_224.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_224.py.debug_pkl
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-    return input
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-    return input
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_236.py
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-class Conv2d(Module):
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-    return input
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-class Conv2d(Module):
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-    return input0
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-class Conv2d(Module):
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-    return input
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-class Conv2d(Module):
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-    return input0
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-    return input0
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_280.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_280.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_281.py
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_281.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_281.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_285.py
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_285.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_285.py.debug_pkl
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-class Conv2d(Module):
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-    return input
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-    return input
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-    return input0
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_300.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_300.py.debug_pkl
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-class Conv2d(Module):
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-    return input
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_308.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_308.py.debug_pkl
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_312.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_312.py.debug_pkl
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-class ConvTranspose2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_328.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_328.py.debug_pkl
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index 57974d82725014f159b90bbc6f4da7e1b896ff44..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_33.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_33.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_33.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_33.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_333.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_333.py
deleted file mode 100644
index 3397dfc9518ab352ce1047aa3f1ddcb79e20869d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_333.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_333.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input0 = torch._convolution(input, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input0
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_333.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_333.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_333.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_339.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_339.py
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index e9f80d2810aaef3fa665494e87504f682544ff22..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_339.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_339.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    Y = torch._convolution(argument_1, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return Y
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_339.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_339.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_339.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_340.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_340.py
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index a33ff4e3ea4992b5a7dc6c7e109f757d3d6b169c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_340.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class ConvTranspose2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_340.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_340.py.debug_pkl
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index 81ae492094561799e18e8520f4c6c04227705d78..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_341.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_341.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_341.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_341.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_348.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_348.py
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index 95d47943eb2d6fc3727cea447e2ed25fec506ceb..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_348.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_348.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_348.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_364.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_364.py
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_364.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_364.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_364.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_37.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_37.Conv2d,
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-    bias = self.bias
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_37.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_37.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_380.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_380.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_380.py.debug_pkl
deleted file mode 100644
index cccb6b2b16437f3db2b51fb55303ec497885bbf3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_380.py.debug_pkl
+++ /dev/null
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_396.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_396.py
deleted file mode 100644
index adb17b3fa270b608ce0b9cd8dbe683faee0d41b2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_396.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_396.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_396.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_396.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_41.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_41.py
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index c021130ffc5a2e76fd3fbc34df0eb0493639e539..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_41.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_41.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [3, 3], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_41.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_41.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_41.py.debug_pkl
+++ /dev/null
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_412.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_412.py
deleted file mode 100644
index caff6835bf4cbb1932820596d2c0547d4fa7e47c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_412.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_412.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_412.py.debug_pkl
deleted file mode 100644
index 460eafce32676de63b70fe2b47dc7bc9e3c63454..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_412.py.debug_pkl
+++ /dev/null
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_428.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_428.py
deleted file mode 100644
index d9818c712fb775fefc62a06b33020e931fcd9bae..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_428.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_428.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_428.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_428.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_444.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_444.py
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index 8994117ee936358fd2f27a2aa7d5116c9514e783..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_444.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_444.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_444.py.debug_pkl
deleted file mode 100644
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_45.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_45.py
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_45.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_45.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [5, 5], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_45.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_45.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_45.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_460.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_460.py
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index 50cfa48e281c06ea84bccf71ff751cb9a18cf3c0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_460.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_460.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_460.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_476.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_476.py
deleted file mode 100644
index 681245c6bd2367174242fb17ee725ece4df69997..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_476.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_476.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_476.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_492.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_492.py
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index 3cbac124402a53a5ff424d404104d64df1d32b8d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_492.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_492.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_492.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_5.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_5.py
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_5.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_5.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_5.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_5.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_508.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_508.py
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index 344d38f9c1dab40665d5a5967e59b089e88b3973..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_508.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_508.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_508.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_51.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_51.py
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_51.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_51.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_51.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_51.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_52.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_52.py
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_52.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_52.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_52.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_52.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_523.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_523.py
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_523.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class ConvTranspose2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input0 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input1 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input2 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input3 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input4 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input5 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input6 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_523.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_523.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_524.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_524.py
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index 84d784c6e4886fc1c8587228527dc08572788a3c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_524.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class ConvTranspose2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_524.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_524.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_526.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class ConvTranspose2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(argument_1, weight, bias, [4, 4], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(argument_1, weight, bias, [4, 4], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(argument_1, weight, bias, [4, 4], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(argument_1, weight, bias, [4, 4], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(argument_1, weight, bias, [4, 4], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(argument_1, weight, bias, [4, 4], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(argument_1, weight, bias, [4, 4], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_526.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_526.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_529.py
+++ /dev/null
@@ -1,25 +0,0 @@
-class ConvTranspose2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_529.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1, 1], [1, 1], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_529.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1, 1], [1, 1], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_529.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1, 1], [1, 1], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return _2
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_529.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_529.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_530.py
+++ /dev/null
@@ -1,25 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
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-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_530.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    _1 = torch._convolution(input, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_530.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _2
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_530.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_530.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_531.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_531.py
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_531.py
+++ /dev/null
@@ -1,43 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_531.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    _0 = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_531.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_531.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_531.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_531.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_531.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _5
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_531.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_531.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_532.py
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-class Conv2d(Module):
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-    return input0
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_535.py
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@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_535.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_535.py.debug_pkl
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-class Conv2d(Module):
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-    return input0
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_587.py
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_587.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_587.py.debug_pkl
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-class Conv2d(Module):
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-    return input
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_674.py
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-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
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-    return input
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-class Conv2d(Module):
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-    return input
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_757.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_757.py.debug_pkl
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-class Conv1d(Module):
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-    return input
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+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_860.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [5, 5], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_860.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_860.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_860.py.debug_pkl
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index 09e444e18f4e279c9695eccb19afd2ebe5635f7b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_866.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_866.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input0 = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input0
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_866.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_866.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_866.py.debug_pkl
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index 821d81c8933f5cb29a957a4bc29ce78e3ecdef3a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_867.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_867.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_867.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_867.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_867.py.debug_pkl
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index 6e085c4759c8b3908526b70db3f13143e84a15cf..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_871.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_871.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [2, 2], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_871.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_871.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_875.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_875.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [3, 3], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_875.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_875.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_875.py.debug_pkl
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index 1b1578535c57eaaeda06381b8b12688f10260fca..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_879.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-  weight : Tensor
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-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_879.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_879.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_879.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_885.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
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-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_885.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    input0 = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input0
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_885.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_885.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_885.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_886.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_886.Conv2d,
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_886.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_886.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_89.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_89.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_89.py.debug_pkl
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+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [2, 2], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_890.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_890.py.debug_pkl
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_894.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_894.py.debug_pkl
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+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_898.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_898.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_90.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_90.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
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-    return input0
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_943.py
+++ /dev/null
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-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
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-  weight : Tensor
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-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_943.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
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+++ /dev/null
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-class Conv2d(Module):
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
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-    return input
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-    return input
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-    return input0
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-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [2, 2], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_985.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_985.py.debug_pkl
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index 333cb2ce0749dc711cee4de971ed0ace86e17029..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_985.py.debug_pkl
+++ /dev/null
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index 4938579e866c0b67710fd9e231d00a13b59da185..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_989.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_989.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [3, 3], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_989.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_989.py.debug_pkl
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index 22870e3afc22e8224a4fd93502eb5ab0d97d8789..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_993.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_993.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [5, 5], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_993.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/conv/___torch_mangle_993.py.debug_pkl
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index a101a4022c7c718c124d125ed40fc13d0a866f56..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout.py
+++ /dev/null
@@ -1,26 +0,0 @@
-class Dropout(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.dropout.Dropout,
-    input: Tensor) -> Tensor:
-    return torch.dropout(input, 0., True)
-  def forward1(self: __torch__.torch.nn.modules.dropout.Dropout,
-    input: Tensor) -> Tensor:
-    return torch.dropout(input, 0., True)
-  def forward2(self: __torch__.torch.nn.modules.dropout.Dropout,
-    input: Tensor) -> Tensor:
-    return torch.dropout(input, 0., True)
-  def forward3(self: __torch__.torch.nn.modules.dropout.Dropout,
-    input: Tensor) -> Tensor:
-    return torch.dropout(input, 0., True)
-  def forward4(self: __torch__.torch.nn.modules.dropout.Dropout,
-    input: Tensor) -> Tensor:
-    return torch.dropout(input, 0., True)
-  def forward5(self: __torch__.torch.nn.modules.dropout.Dropout,
-    input: Tensor) -> Tensor:
-    return torch.dropout(input, 0., True)
-  def forward6(self: __torch__.torch.nn.modules.dropout.Dropout,
-    input: Tensor) -> Tensor:
-    return torch.dropout(input, 0., True)
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout.py.debug_pkl
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index 96cc31f1287bb8e5ac7c8bf893f1bd806685eae9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_345.py
+++ /dev/null
@@ -1,47 +0,0 @@
-class Dropout(Module):
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-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward1(self: __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward2(self: __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward3(self: __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward4(self: __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward5(self: __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward6(self: __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward7(self: __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward8(self: __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward9(self: __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward10(self: __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward11(self: __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward12(self: __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout,
-    argument_1: Tensor) -> Tensor:
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-  def forward13(self: __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_345.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_345.py.debug_pkl
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+++ /dev/null
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-class Dropout(Module):
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-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
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-  def forward1(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
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-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward3(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward4(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward5(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward6(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
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-    return torch.dropout(argument_1, 0., True)
-  def forward7(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward8(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward9(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward10(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward11(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
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-    return torch.dropout(argument_1, 0., True)
-  def forward13(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_359.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_359.py.debug_pkl
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+++ /dev/null
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-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
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-    return torch.dropout(argument_1, 0., True)
-  def forward1(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
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-  def forward2(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward3(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward4(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward5(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
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-    return torch.dropout(argument_1, 0., True)
-  def forward6(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
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-    return torch.dropout(argument_1, 0., True)
-  def forward8(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
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-    return torch.dropout(argument_1, 0., True)
-  def forward9(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward10(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
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-    return torch.dropout(argument_1, 0., True)
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-    return torch.dropout(argument_1, 0., True)
-  def forward12(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
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-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_375.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_375.py.debug_pkl
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-  def forward4(self: __torch__.torch.nn.modules.dropout.___torch_mangle_391.Dropout,
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-    return torch.dropout(argument_1, 0., True)
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-    return torch.dropout(argument_1, 0., True)
-  def forward9(self: __torch__.torch.nn.modules.dropout.___torch_mangle_391.Dropout,
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-    return torch.dropout(argument_1, 0., True)
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_391.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_391.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_407.py
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-class Dropout(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward1(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward2(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward3(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward4(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward5(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward6(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward7(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward8(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward9(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward10(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward11(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
-  def forward12(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
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-  def forward13(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_407.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_407.py.debug_pkl
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-class Dropout(Module):
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-  def forward4(self: __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout,
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-  def forward5(self: __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout,
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-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_423.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_423.py.debug_pkl
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-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
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-  def forward2(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
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-  def forward4(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
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-    return torch.dropout(argument_1, 0., True)
-  def forward5(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
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-    return torch.dropout(argument_1, 0., True)
-  def forward6(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
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-  def forward10(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
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-    return torch.dropout(argument_1, 0., True)
-  def forward11(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
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-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_439.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_439.py.debug_pkl
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-  def forward4(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
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-  def forward5(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_455.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_455.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_471.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/dropout/___torch_mangle_471.py.debug_pkl
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-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_791.Dropout,
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-    argument_1: Tensor) -> Tensor:
-    return torch.dropout(argument_1, 0., True)
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-class Identity(Module):
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-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
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-    return input
-  def forward1(self: __torch__.torch.nn.modules.linear.Linear,
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-    return input
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-    return input
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-    return input
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-class Identity(Module):
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-class Linear(Module):
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-    argument_1: Tensor) -> Tensor:
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-    return input
-  def forward1(self: __torch__.torch.nn.modules.linear.___torch_mangle_355.Linear,
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-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_355.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_355.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_355.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_355.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_355.py.debug_pkl
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+++ /dev/null
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_357.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_366.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class Identity(Module):
-  __parameters__ = []
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_366.Identity) -> NoneType:
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-    return None
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-    return None
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-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_366.Identity) -> NoneType:
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+++ /dev/null
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-class NonDynamicallyQuantizableLinear(Module):
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-  __buffers__ = []
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_371.py
+++ /dev/null
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-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
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-  weight : Tensor
-  bias : Tensor
-  training : bool
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-    return input
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-    bias = self.bias
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-    return input
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-    return input
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_371.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_371.py.debug_pkl
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-class Linear(Module):
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_382.py
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-class Identity(Module):
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-class NonDynamicallyQuantizableLinear(Module):
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_387.py
+++ /dev/null
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-class Linear(Module):
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-  __buffers__ = []
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-  def forward2(self: __torch__.torch.nn.modules.linear.___torch_mangle_387.Linear,
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-    return input
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-    bias = self.bias
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_387.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_387.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_398.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_398.py
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index f2845987b2e8faa96a7710ab1612e01422ce1a9a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_398.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class Identity(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward1(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward2(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward7(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward8(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward9(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward10(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward11(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward12(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward13(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_398.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_398.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_398.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_399.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_399.py
deleted file mode 100644
index f7e0198a95b47fd9412f1fc49af5795f0a627490..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_399.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class NonDynamicallyQuantizableLinear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_399.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_399.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_399.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_403.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_403.py
deleted file mode 100644
index a3e4eb0cfdc103a7b870e0e836c7e6491c4303c0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_403.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_403.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward1(self: __torch__.torch.nn.modules.linear.___torch_mangle_403.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward2(self: __torch__.torch.nn.modules.linear.___torch_mangle_403.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_403.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_403.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_403.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_403.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_403.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_403.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_403.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_405.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_405.py
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_405.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_405.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_405.py.debug_pkl
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+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_414.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_414.py
deleted file mode 100644
index 649a656529e8700a96bc8882be1c3197397a534a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_414.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class Identity(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward1(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward2(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward7(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward8(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward9(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward10(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward11(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward12(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward13(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_414.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_414.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_414.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_415.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_415.py
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_415.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class NonDynamicallyQuantizableLinear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_415.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_415.py.debug_pkl
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+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_419.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_419.py
deleted file mode 100644
index 9f513431e73ae472f73e52cb31a2d3bed3f611ef..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_419.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward1(self: __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward2(self: __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_419.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_419.py.debug_pkl
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+++ /dev/null
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_421.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_421.py
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_421.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_421.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_421.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_430.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_430.py
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_430.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class Identity(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity) -> NoneType:
-    return None
-  def forward1(self: __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity) -> NoneType:
-    return None
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-    return None
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity) -> NoneType:
-    return None
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity) -> NoneType:
-    return None
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity) -> NoneType:
-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity) -> NoneType:
-    return None
-  def forward7(self: __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity) -> NoneType:
-    return None
-  def forward8(self: __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity) -> NoneType:
-    return None
-  def forward9(self: __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity) -> NoneType:
-    return None
-  def forward10(self: __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity) -> NoneType:
-    return None
-  def forward11(self: __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity) -> NoneType:
-    return None
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-    return None
-  def forward13(self: __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity) -> NoneType:
-    return None
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_430.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_430.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_431.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class NonDynamicallyQuantizableLinear(Module):
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-  __buffers__ = []
-  weight : Tensor
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_435.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward1(self: __torch__.torch.nn.modules.linear.___torch_mangle_435.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_435.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
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-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_435.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_435.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_435.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_435.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_435.py.debug_pkl
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-class Linear(Module):
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_446.py
+++ /dev/null
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-class Identity(Module):
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-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_446.Identity) -> NoneType:
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-    return None
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-    return None
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-    return None
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-    return None
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-    return None
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_447.py
+++ /dev/null
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-class NonDynamicallyQuantizableLinear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  training : bool
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_451.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
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-    return input
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-    return input
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-    bias = self.bias
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-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_451.Linear,
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-    bias = self.bias
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-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_451.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_451.Linear,
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-    return input
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_451.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_451.py.debug_pkl
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-class Linear(Module):
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_462.py
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-class Identity(Module):
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-    return None
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-    return None
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-    return None
-  def forward7(self: __torch__.torch.nn.modules.linear.___torch_mangle_462.Identity) -> NoneType:
-    return None
-  def forward8(self: __torch__.torch.nn.modules.linear.___torch_mangle_462.Identity) -> NoneType:
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-  def forward9(self: __torch__.torch.nn.modules.linear.___torch_mangle_462.Identity) -> NoneType:
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_462.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_462.py.debug_pkl
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+++ /dev/null
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-class NonDynamicallyQuantizableLinear(Module):
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_467.py
+++ /dev/null
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-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
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-  weight : Tensor
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-    return input
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-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_467.Linear,
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-    bias = self.bias
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-    return input
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_467.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_467.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_469.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_469.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_478.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class Identity(Module):
-  __parameters__ = []
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_478.Identity) -> NoneType:
-    return None
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-    return None
-  def forward2(self: __torch__.torch.nn.modules.linear.___torch_mangle_478.Identity) -> NoneType:
-    return None
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_478.Identity) -> NoneType:
-    return None
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_478.Identity) -> NoneType:
-    return None
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_478.Identity) -> NoneType:
-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_478.Identity) -> NoneType:
-    return None
-  def forward7(self: __torch__.torch.nn.modules.linear.___torch_mangle_478.Identity) -> NoneType:
-    return None
-  def forward8(self: __torch__.torch.nn.modules.linear.___torch_mangle_478.Identity) -> NoneType:
-    return None
-  def forward9(self: __torch__.torch.nn.modules.linear.___torch_mangle_478.Identity) -> NoneType:
-    return None
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-    return None
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-    return None
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-    return None
-  def forward13(self: __torch__.torch.nn.modules.linear.___torch_mangle_478.Identity) -> NoneType:
-    return None
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_479.py
+++ /dev/null
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-class NonDynamicallyQuantizableLinear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_479.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_479.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_483.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
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-    bias = self.bias
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_483.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_483.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_483.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_483.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_483.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_483.py.debug_pkl
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-class Linear(Module):
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_494.py
+++ /dev/null
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-class Identity(Module):
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-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity) -> NoneType:
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-    return None
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity) -> NoneType:
-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity) -> NoneType:
-    return None
-  def forward7(self: __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity) -> NoneType:
-    return None
-  def forward8(self: __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity) -> NoneType:
-    return None
-  def forward9(self: __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity) -> NoneType:
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-  def forward13(self: __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity) -> NoneType:
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+++ /dev/null
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-class NonDynamicallyQuantizableLinear(Module):
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_499.py
+++ /dev/null
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-class Linear(Module):
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-  __buffers__ = []
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-    return input
-  def forward2(self: __torch__.torch.nn.modules.linear.___torch_mangle_499.Linear,
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-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_499.Linear,
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-    bias = self.bias
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-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_499.Linear,
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-    bias = self.bias
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-    return input
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-    bias = self.bias
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-    return input
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_499.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_499.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_511.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class NonDynamicallyQuantizableLinear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_511.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_511.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_515.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_515.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward1(self: __torch__.torch.nn.modules.linear.___torch_mangle_515.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward2(self: __torch__.torch.nn.modules.linear.___torch_mangle_515.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_515.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_515.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_515.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_515.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_515.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_515.py.debug_pkl
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-class Linear(Module):
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-    return None
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-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_528.Identity) -> NoneType:
-    return None
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-class Linear(Module):
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-class Linear(Module):
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_709.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/linear/___torch_mangle_709.py.debug_pkl
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-    return x
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-    return x
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-    argument_1: Tensor) -> Tensor:
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-    return x
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-    argument_1: Tensor) -> Tensor:
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-    return x
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-    return x
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_1.py
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_1.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_1.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_1001.py
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-class GroupNorm(Module):
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_141.py
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_141.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_141.py.debug_pkl
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-class GroupNorm(Module):
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_148.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_148.py.debug_pkl
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_252.py
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-    argument_1: Tensor) -> Tensor:
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_252.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_252.py.debug_pkl
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_309.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_309.py
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_309.py
+++ /dev/null
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_309.GroupNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_309.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_309.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_309.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_313.py
+++ /dev/null
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_313.GroupNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_313.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_313.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_319.py
+++ /dev/null
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
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-  _is_full_backward_hook : Optional[bool]
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch.group_norm(argument_1, 2, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_319.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_319.py.debug_pkl
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-class GroupNorm(Module):
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-    argument_1: Tensor) -> Tensor:
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_324.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_324.py.debug_pkl
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-class GroupNorm(Module):
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-    return input
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+++ /dev/null
@@ -1,13 +0,0 @@
-class GroupNorm(Module):
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-  weight : Tensor
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_334.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_334.py.debug_pkl
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+++ /dev/null
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-class GroupNorm(Module):
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_34.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_34.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_343.py
+++ /dev/null
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-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
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-    return input0
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_343.LayerNorm,
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-    return input1
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_343.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input2 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input2
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_343.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input3 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input3
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_343.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input4 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input4
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_343.LayerNorm,
-    input: Tensor) -> Tensor:
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-    return input5
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_343.LayerNorm,
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-    bias = self.bias
-    weight = self.weight
-    input6 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_343.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_343.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_346.py
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@@ -1,49 +0,0 @@
-class LayerNorm(Module):
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-    return x
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_346.LayerNorm,
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-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_346.LayerNorm,
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-    bias = self.bias
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-    return x
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_346.LayerNorm,
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-    return x
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_346.LayerNorm,
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-    bias = self.bias
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-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_346.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_346.py.debug_pkl
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+++ /dev/null
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-class LayerNorm(Module):
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-    return input1
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm,
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-    return input2
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm,
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-    return input3
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm,
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-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm,
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-    input6 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_354.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_354.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_362.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_362.py
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_362.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_362.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_362.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_370.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_370.py
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_370.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_370.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input0 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input0
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_370.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input1 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input1
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_370.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input2 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input2
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_370.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input3 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input3
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_370.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input4 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input4
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_370.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input5 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input5
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_370.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input6 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_370.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_370.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_378.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_378.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_378.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_378.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_378.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_378.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_378.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_378.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_378.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_378.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_38.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_38.py
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_38.py
+++ /dev/null
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_38.GroupNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_38.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_38.py.debug_pkl
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index 58fd8748802acebbabb97ed036e126a638888d59..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_386.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_386.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input0 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input0
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_386.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input1 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input1
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_386.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input2 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input2
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_386.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input3 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input3
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_386.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input4 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input4
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_386.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return input5
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_386.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input6 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_386.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_386.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_394.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
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-    return x
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_394.LayerNorm,
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-    return x
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_394.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_394.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
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-    return x
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_394.LayerNorm,
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-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_394.LayerNorm,
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-    bias = self.bias
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-    return x
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_394.LayerNorm,
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-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_394.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_394.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_402.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_402.py
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_402.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
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-    return input0
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
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-    bias = self.bias
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-    return input1
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    input2 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input2
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return input3
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
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-    bias = self.bias
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-    return input4
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
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-    bias = self.bias
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-    return input5
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
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-    bias = self.bias
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-    input6 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_402.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_402.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_410.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_410.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_410.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_418.py
+++ /dev/null
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-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_418.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    input0 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input0
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_418.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input1 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input1
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_418.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input2 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input2
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_418.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return input3
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_418.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input4 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input4
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_418.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return input5
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_418.LayerNorm,
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-    bias = self.bias
-    weight = self.weight
-    input6 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_418.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_418.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_42.py
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-class GroupNorm(Module):
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-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_42.GroupNorm,
-    argument_1: Tensor) -> Tensor:
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_42.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_42.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_426.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
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-    bias = self.bias
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-    return x
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_426.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return x
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_426.LayerNorm,
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-    return x
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_426.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return x
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_426.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
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-    return x
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_426.LayerNorm,
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-    return x
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_426.LayerNorm,
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-    bias = self.bias
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-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
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+++ /dev/null
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-class LayerNorm(Module):
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-    return input1
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_434.LayerNorm,
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-    bias = self.bias
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-    return input2
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_434.LayerNorm,
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-    bias = self.bias
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-    return input3
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_434.LayerNorm,
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-    return input4
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_434.LayerNorm,
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-    return input5
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-    bias = self.bias
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-    input6 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_434.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_434.py.debug_pkl
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+++ /dev/null
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-class LayerNorm(Module):
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-    return x
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_442.LayerNorm,
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-    return x
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_442.LayerNorm,
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-    return x
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-    return x
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_442.LayerNorm,
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-    return x
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_442.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_442.py.debug_pkl
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-class LayerNorm(Module):
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-    return input6
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_458.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_458.py
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_458.py
+++ /dev/null
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-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_458.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_458.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_458.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_458.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_458.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_458.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return x
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_458.LayerNorm,
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-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_466.py
+++ /dev/null
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-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
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-  bias : Tensor
-  training : bool
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-    return input0
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_466.LayerNorm,
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-    return input1
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-    return input2
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_466.LayerNorm,
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-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_466.LayerNorm,
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-    return input4
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-    return input5
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_466.LayerNorm,
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-    bias = self.bias
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-    return input6
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_466.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_466.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_474.py
+++ /dev/null
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-class LayerNorm(Module):
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-    return x
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-    return x
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-    return x
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-    return x
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-    return x
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+++ /dev/null
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-class LayerNorm(Module):
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-    return input2
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-    return input6
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-class LayerNorm(Module):
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-class LayerNorm(Module):
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-    return input
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-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_975.GroupNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_975.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_975.py.debug_pkl
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+++ /dev/null
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_982.GroupNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_982.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_982.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_986.py
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
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-  bias : Tensor
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-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_986.GroupNorm,
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-    bias = self.bias
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_986.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_986.py.debug_pkl
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-class GroupNorm(Module):
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-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_99.GroupNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_99.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_99.py.debug_pkl
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-class GroupNorm(Module):
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-    return input
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-class GroupNorm(Module):
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-    argument_1: Tensor) -> Tensor:
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_994.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/normalization/___torch_mangle_994.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling.py
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-class AdaptiveAvgPool1d(Module):
-  __parameters__ = []
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
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-    return input
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-    argument_1: Tensor) -> Tensor:
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-    return input
-  def forward2(self: __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
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-    return input
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-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
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-    argument_1: Tensor) -> Tensor:
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-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling.py.debug_pkl
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-class AdaptiveAvgPool1d(Module):
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-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
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-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_358.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_358.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_374.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_374.py.debug_pkl
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-  def forward1(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward2(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward4(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_406.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_406.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_406.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_422.py b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_422.py
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index 1125385dc12600285c47a6c1403b4f26abe9fc91..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_422.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class AdaptiveAvgPool1d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward1(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward2(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward4(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_422.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_422.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_438.py
+++ /dev/null
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-class AdaptiveAvgPool1d(Module):
-  __parameters__ = []
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_438.AdaptiveAvgPool1d,
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-    argument_1: Tensor) -> Tensor:
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-    return input
-  def forward2(self: __torch__.torch.nn.modules.pooling.___torch_mangle_438.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.___torch_mangle_438.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward4(self: __torch__.torch.nn.modules.pooling.___torch_mangle_438.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_438.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.___torch_mangle_438.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_438.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_438.py.debug_pkl
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index 009c360ec1c2ba95611ebb22d668593524ff863e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_454.py
+++ /dev/null
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-class AdaptiveAvgPool1d(Module):
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-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_454.AdaptiveAvgPool1d,
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-    return input
-  def forward2(self: __torch__.torch.nn.modules.pooling.___torch_mangle_454.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.___torch_mangle_454.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward4(self: __torch__.torch.nn.modules.pooling.___torch_mangle_454.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_454.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.___torch_mangle_454.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_454.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_454.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_470.py
+++ /dev/null
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-class AdaptiveAvgPool1d(Module):
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-  def forward2(self: __torch__.torch.nn.modules.pooling.___torch_mangle_470.AdaptiveAvgPool1d,
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-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.___torch_mangle_470.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward4(self: __torch__.torch.nn.modules.pooling.___torch_mangle_470.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_470.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
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-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_470.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_470.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_486.py
+++ /dev/null
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-    return input
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-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_486.AdaptiveAvgPool1d,
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-    return input
diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_486.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_486.py.debug_pkl
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--- a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_502.py
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-    return input
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diff --git a/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_502.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/torch/nn/modules/pooling/___torch_mangle_502.py.debug_pkl
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-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_518.AdaptiveAvgPool1d,
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-    input: Tensor) -> Tensor:
-    act = self.act
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-    return (act).forward(_1, )
-class Decoder(Module):
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  dec : __torch__.torch.nn.modules.container.___torch_mangle_1010.Sequential
-  readout : __torch__.torch.nn.modules.conv.___torch_mangle_1011.Conv2d
-  def forward(self: __torch__.utils.Decoder,
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+++ /dev/null
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diff --git a/xe_weights14/glonet_p1/code/__torch__/utils/___torch_mangle_1003.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/utils/___torch_mangle_1003.py.debug_pkl
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-  conv : __torch__.utils.___torch_mangle_1003.BasicConv2d
-  def forward(self: __torch__.utils.___torch_mangle_1004.ConvSC,
-    input: Tensor) -> Tensor:
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diff --git a/xe_weights14/glonet_p1/code/__torch__/utils/___torch_mangle_1004.py.debug_pkl b/xe_weights14/glonet_p1/code/__torch__/utils/___torch_mangle_1004.py.debug_pkl
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diff --git a/xe_weights14/glonet_p1/traced_inputs.pkl b/xe_weights14/glonet_p1/traced_inputs.pkl
deleted file mode 100644
index 07399dd082bed1462c41f358fe46499e7039919a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p1/traced_inputs.pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2a29ffe52619c47ac9552ea60e7ebec83ad755683f4aac674db4756ee5d64e70
-size 285
diff --git a/xe_weights14/glonet_p1/traced_inputs/0 b/xe_weights14/glonet_p1/traced_inputs/0
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/xe_weights14/glonet_p2/.data/serialization_id b/xe_weights14/glonet_p2/.data/serialization_id
deleted file mode 100644
index 7b2647d04b4c24546bb685480bc80cdb7583fe46..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/.data/serialization_id
+++ /dev/null
@@ -1 +0,0 @@
-0371189840256180555108623164620490996820
\ No newline at end of file
diff --git a/xe_weights14/glonet_p2/.data/version b/xe_weights14/glonet_p2/.data/version
deleted file mode 100644
index f599e28b8ab0d8c9c57a486c89c4a5132dcbd3b2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/.data/version
+++ /dev/null
@@ -1 +0,0 @@
-10
diff --git a/xe_weights14/glonet_p2/byteorder b/xe_weights14/glonet_p2/byteorder
deleted file mode 100644
index a06f92f60ada3d71348371f5efc12c5ea640376a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/byteorder
+++ /dev/null
@@ -1 +0,0 @@
-little
\ No newline at end of file
diff --git a/xe_weights14/glonet_p2/code/__torch__.py b/xe_weights14/glonet_p2/code/__torch__.py
deleted file mode 100644
index 20ba97e6f42485c2f7268f072d829f02326dd237..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__.py
+++ /dev/null
@@ -1,209 +0,0 @@
-class SelectiveFilterLayer2(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.SelectiveFilterLayer2,
-    argument_1: Tensor) -> Tensor:
-    x_filtered = torch.clone(argument_1)
-    _0 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _1 = torch.slice(_0, 1, 0, 9223372036854775807)
-    _2 = torch.slice(torch.select(_1, 2, 0), 2, 0, 9223372036854775807)
-    _3 = torch.slice(_2, 3, 0, 9223372036854775807)
-    _4 = torch.clamp(_3, -1.5186430099999999, 2.0511626700000001)
-    _5 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _6 = torch.slice(_5, 1, 0, 9223372036854775807)
-    _7 = torch.slice(torch.select(_6, 2, 0), 2, 0, 9223372036854775807)
-    _8 = torch.slice(_7, 3, 0, 9223372036854775807)
-    _9 = torch.copy_(_8, _4)
-    _10 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _11 = torch.slice(_10, 1, 0, 9223372036854775807)
-    _12 = torch.slice(torch.select(_11, 2, 10), 2, 0, 9223372036854775807)
-    _13 = torch.slice(_12, 3, 0, 9223372036854775807)
-    _14 = torch.clamp(_13, -24.692883040000002, 4.1726714999999999)
-    _15 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _16 = torch.slice(_15, 1, 0, 9223372036854775807)
-    _17 = torch.slice(torch.select(_16, 2, 10), 2, 0, 9223372036854775807)
-    _18 = torch.slice(_17, 3, 0, 9223372036854775807)
-    _19 = torch.copy_(_18, _14)
-    _20 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _21 = torch.slice(_20, 1, 0, 9223372036854775807)
-    _22 = torch.slice(torch.select(_21, 2, 1), 2, 0, 9223372036854775807)
-    _23 = torch.slice(_22, 3, 0, 9223372036854775807)
-    _24 = torch.clamp(_23, -1.68452522, 2.5031795300000002)
-    _25 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _26 = torch.slice(_25, 1, 0, 9223372036854775807)
-    _27 = torch.slice(torch.select(_26, 2, 1), 2, 0, 9223372036854775807)
-    _28 = torch.slice(_27, 3, 0, 9223372036854775807)
-    _29 = torch.copy_(_28, _24)
-    _30 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _31 = torch.slice(_30, 1, 0, 9223372036854775807)
-    _32 = torch.slice(torch.select(_31, 2, 11), 2, 0, 9223372036854775807)
-    _33 = torch.slice(_32, 3, 0, 9223372036854775807)
-    _34 = torch.clamp(_33, -24.979283460000001, 5.8817625900000001)
-    _35 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _36 = torch.slice(_35, 1, 0, 9223372036854775807)
-    _37 = torch.slice(torch.select(_36, 2, 11), 2, 0, 9223372036854775807)
-    _38 = torch.slice(_37, 3, 0, 9223372036854775807)
-    _39 = torch.copy_(_38, _34)
-    _40 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _41 = torch.slice(_40, 1, 0, 9223372036854775807)
-    _42 = torch.slice(torch.select(_41, 2, 2), 2, 0, 9223372036854775807)
-    _43 = torch.slice(_42, 3, 0, 9223372036854775807)
-    _44 = torch.clamp(_43, -1.8796933499999999, 2.9674439499999998)
-    _45 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _46 = torch.slice(_45, 1, 0, 9223372036854775807)
-    _47 = torch.slice(torch.select(_46, 2, 2), 2, 0, 9223372036854775807)
-    _48 = torch.slice(_47, 3, 0, 9223372036854775807)
-    _49 = torch.copy_(_48, _44)
-    _50 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _51 = torch.slice(_50, 1, 0, 9223372036854775807)
-    _52 = torch.slice(torch.select(_51, 2, 12), 2, 0, 9223372036854775807)
-    _53 = torch.slice(_52, 3, 0, 9223372036854775807)
-    _54 = torch.clamp(_53, -18.813885840000001, 7.6746507800000003)
-    _55 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _56 = torch.slice(_55, 1, 0, 9223372036854775807)
-    _57 = torch.slice(torch.select(_56, 2, 12), 2, 0, 9223372036854775807)
-    _58 = torch.slice(_57, 3, 0, 9223372036854775807)
-    _59 = torch.copy_(_58, _54)
-    _60 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _61 = torch.slice(_60, 1, 0, 9223372036854775807)
-    _62 = torch.slice(torch.select(_61, 2, 3), 2, 0, 9223372036854775807)
-    _63 = torch.slice(_62, 3, 0, 9223372036854775807)
-    _64 = torch.clamp(_63, -2.1027860899999999, 3.3004108400000001)
-    _65 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _66 = torch.slice(_65, 1, 0, 9223372036854775807)
-    _67 = torch.slice(torch.select(_66, 2, 3), 2, 0, 9223372036854775807)
-    _68 = torch.slice(_67, 3, 0, 9223372036854775807)
-    _69 = torch.copy_(_68, _64)
-    _70 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _71 = torch.slice(_70, 1, 0, 9223372036854775807)
-    _72 = torch.slice(torch.select(_71, 2, 13), 2, 0, 9223372036854775807)
-    _73 = torch.slice(_72, 3, 0, 9223372036854775807)
-    _74 = torch.clamp(_73, -20.123415959999999, 8.3328451099999992)
-    _75 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _76 = torch.slice(_75, 1, 0, 9223372036854775807)
-    _77 = torch.slice(torch.select(_76, 2, 13), 2, 0, 9223372036854775807)
-    _78 = torch.slice(_77, 3, 0, 9223372036854775807)
-    _79 = torch.copy_(_78, _74)
-    _80 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _81 = torch.slice(_80, 1, 0, 9223372036854775807)
-    _82 = torch.slice(torch.select(_81, 2, 4), 2, 0, 9223372036854775807)
-    _83 = torch.slice(_82, 3, 0, 9223372036854775807)
-    _84 = torch.clamp(_83, -2.2117279999999999, 3.8506422100000002)
-    _85 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _86 = torch.slice(_85, 1, 0, 9223372036854775807)
-    _87 = torch.slice(torch.select(_86, 2, 4), 2, 0, 9223372036854775807)
-    _88 = torch.slice(_87, 3, 0, 9223372036854775807)
-    _89 = torch.copy_(_88, _84)
-    _90 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _91 = torch.slice(_90, 1, 0, 9223372036854775807)
-    _92 = torch.slice(torch.select(_91, 2, 14), 2, 0, 9223372036854775807)
-    _93 = torch.slice(_92, 3, 0, 9223372036854775807)
-    _94 = torch.clamp(_93, -20.32124576, 8.8710279599999993)
-    _95 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _96 = torch.slice(_95, 1, 0, 9223372036854775807)
-    _97 = torch.slice(torch.select(_96, 2, 14), 2, 0, 9223372036854775807)
-    _98 = torch.slice(_97, 3, 0, 9223372036854775807)
-    _99 = torch.copy_(_98, _94)
-    _100 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _101 = torch.slice(_100, 1, 0, 9223372036854775807)
-    _102 = torch.slice(torch.select(_101, 2, 5), 2, 0, 9223372036854775807)
-    _103 = torch.slice(_102, 3, 0, 9223372036854775807)
-    _104 = torch.clamp(_103, -2.32218144, 4.4685749299999999)
-    _105 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _106 = torch.slice(_105, 1, 0, 9223372036854775807)
-    _107 = torch.slice(torch.select(_106, 2, 5), 2, 0, 9223372036854775807)
-    _108 = torch.slice(_107, 3, 0, 9223372036854775807)
-    _109 = torch.copy_(_108, _104)
-    _110 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _111 = torch.slice(_110, 1, 0, 9223372036854775807)
-    _112 = torch.slice(torch.select(_111, 2, 15), 2, 0, 9223372036854775807)
-    _113 = torch.slice(_112, 3, 0, 9223372036854775807)
-    _114 = torch.clamp(_113, -20.635238699999999, 9.3682492400000008)
-    _115 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _116 = torch.slice(_115, 1, 0, 9223372036854775807)
-    _117 = torch.slice(torch.select(_116, 2, 15), 2, 0, 9223372036854775807)
-    _118 = torch.slice(_117, 3, 0, 9223372036854775807)
-    _119 = torch.copy_(_118, _114)
-    _120 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _121 = torch.slice(_120, 1, 0, 9223372036854775807)
-    _122 = torch.slice(torch.select(_121, 2, 6), 2, 0, 9223372036854775807)
-    _123 = torch.slice(_122, 3, 0, 9223372036854775807)
-    _124 = torch.clamp(_123, -1.46312617, 1.05105242)
-    _125 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _126 = torch.slice(_125, 1, 0, 9223372036854775807)
-    _127 = torch.slice(torch.select(_126, 2, 6), 2, 0, 9223372036854775807)
-    _128 = torch.slice(_127, 3, 0, 9223372036854775807)
-    _129 = torch.copy_(_128, _124)
-    _130 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _131 = torch.slice(_130, 1, 0, 9223372036854775807)
-    _132 = torch.slice(torch.select(_131, 2, 16), 2, 0, 9223372036854775807)
-    _133 = torch.slice(_132, 3, 0, 9223372036854775807)
-    _134 = torch.clamp(_133, -8.5287974799999997, 4.1170337200000002)
-    _135 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _136 = torch.slice(_135, 1, 0, 9223372036854775807)
-    _137 = torch.slice(torch.select(_136, 2, 16), 2, 0, 9223372036854775807)
-    _138 = torch.slice(_137, 3, 0, 9223372036854775807)
-    _139 = torch.copy_(_138, _134)
-    _140 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _141 = torch.slice(_140, 1, 0, 9223372036854775807)
-    _142 = torch.slice(torch.select(_141, 2, 7), 2, 0, 9223372036854775807)
-    _143 = torch.slice(_142, 3, 0, 9223372036854775807)
-    _144 = torch.clamp(_143, -2.4136451499999998, 5.1300334699999999)
-    _145 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _146 = torch.slice(_145, 1, 0, 9223372036854775807)
-    _147 = torch.slice(torch.select(_146, 2, 7), 2, 0, 9223372036854775807)
-    _148 = torch.slice(_147, 3, 0, 9223372036854775807)
-    _149 = torch.copy_(_148, _144)
-    _150 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _151 = torch.slice(_150, 1, 0, 9223372036854775807)
-    _152 = torch.slice(torch.select(_151, 2, 17), 2, 0, 9223372036854775807)
-    _153 = torch.slice(_152, 3, 0, 9223372036854775807)
-    _154 = torch.clamp(_153, -20.971199729999999, 9.9119432500000002)
-    _155 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _156 = torch.slice(_155, 1, 0, 9223372036854775807)
-    _157 = torch.slice(torch.select(_156, 2, 17), 2, 0, 9223372036854775807)
-    _158 = torch.slice(_157, 3, 0, 9223372036854775807)
-    _159 = torch.copy_(_158, _154)
-    _160 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _161 = torch.slice(_160, 1, 0, 9223372036854775807)
-    _162 = torch.slice(torch.select(_161, 2, 8), 2, 0, 9223372036854775807)
-    _163 = torch.slice(_162, 3, 0, 9223372036854775807)
-    _164 = torch.clamp(_163, -2.3799797300000001, 5.0342985599999999)
-    _165 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _166 = torch.slice(_165, 1, 0, 9223372036854775807)
-    _167 = torch.slice(torch.select(_166, 2, 8), 2, 0, 9223372036854775807)
-    _168 = torch.slice(_167, 3, 0, 9223372036854775807)
-    _169 = torch.copy_(_168, _164)
-    _170 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _171 = torch.slice(_170, 1, 0, 9223372036854775807)
-    _172 = torch.slice(torch.select(_171, 2, 18), 2, 0, 9223372036854775807)
-    _173 = torch.slice(_172, 3, 0, 9223372036854775807)
-    _174 = torch.clamp(_173, -21.96210318, 10.50929116)
-    _175 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _176 = torch.slice(_175, 1, 0, 9223372036854775807)
-    _177 = torch.slice(torch.select(_176, 2, 18), 2, 0, 9223372036854775807)
-    _178 = torch.slice(_177, 3, 0, 9223372036854775807)
-    _179 = torch.copy_(_178, _174)
-    _180 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _181 = torch.slice(_180, 1, 0, 9223372036854775807)
-    _182 = torch.slice(torch.select(_181, 2, 9), 2, 0, 9223372036854775807)
-    _183 = torch.slice(_182, 3, 0, 9223372036854775807)
-    _184 = torch.clamp(_183, -2.53711412, 5.9242855600000004)
-    _185 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _186 = torch.slice(_185, 1, 0, 9223372036854775807)
-    _187 = torch.slice(torch.select(_186, 2, 9), 2, 0, 9223372036854775807)
-    _188 = torch.slice(_187, 3, 0, 9223372036854775807)
-    _189 = torch.copy_(_188, _184)
-    _190 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _191 = torch.slice(_190, 1, 0, 9223372036854775807)
-    _192 = torch.slice(torch.select(_191, 2, 19), 2, 0, 9223372036854775807)
-    _193 = torch.slice(_192, 3, 0, 9223372036854775807)
-    _194 = torch.clamp(_193, -23.075551579999999, 11.140115829999999)
-    _195 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _196 = torch.slice(_195, 1, 0, 9223372036854775807)
-    _197 = torch.slice(torch.select(_196, 2, 19), 2, 0, 9223372036854775807)
-    _198 = torch.slice(_197, 3, 0, 9223372036854775807)
-    _199 = torch.copy_(_198, _194)
-    return x_filtered
diff --git a/xe_weights14/glonet_p2/code/__torch__.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__.py.debug_pkl
deleted file mode 100644
index dd2e68fb37d5b68f676146d6c2a06d2d1f672ca7..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:682ca002cac384c49d5ad2385045897c22452455557bb6240ed5ee2f4c6e4452
-size 59492
diff --git a/xe_weights14/glonet_p2/code/__torch__/NN.py b/xe_weights14/glonet_p2/code/__torch__/NN.py
deleted file mode 100644
index 43ced0fc9300ec2722fc5a0360a8054be85f7711..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NN.py
+++ /dev/null
@@ -1,146 +0,0 @@
-class Local_CNN_Branch(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  upconv : __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d
-  def forward(self: __torch__.NN.Local_CNN_Branch,
-    x_raw: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x_raw, 0))
-    _0 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x_raw, 1))
-    _1 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x_raw, 2))
-    _2 = int(C)
-    _3 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x_raw, 3))
-    _4 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x_raw, 4))
-    input = torch.view(x_raw, [-1, _3, _4, int(W)])
-    _5 = (upconv).forward(input, )
-    _6 = ops.prim.NumToTensor(torch.size(_5, 2))
-    _7 = int(_6)
-    _8 = ops.prim.NumToTensor(torch.size(_5, 3))
-    lc_features = torch.view(_5, [_0, _1, _2, _7, int(_8)])
-    return lc_features
-  def forward1(self: __torch__.NN.Local_CNN_Branch,
-    x: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _9 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _10 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _11 = int(C)
-    _12 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _13 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    input = torch.view(x, [-1, _12, _13, int(W)])
-    _14 = (upconv).forward1(input, )
-    _15 = ops.prim.NumToTensor(torch.size(_14, 2))
-    _16 = int(_15)
-    _17 = ops.prim.NumToTensor(torch.size(_14, 3))
-    lc_features = torch.view(_14, [_9, _10, _11, _16, int(_17)])
-    return lc_features
-  def forward2(self: __torch__.NN.Local_CNN_Branch,
-    x: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _18 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _19 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _20 = int(C)
-    _21 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _22 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    input = torch.view(x, [-1, _21, _22, int(W)])
-    _23 = (upconv).forward2(input, )
-    _24 = ops.prim.NumToTensor(torch.size(_23, 2))
-    _25 = int(_24)
-    _26 = ops.prim.NumToTensor(torch.size(_23, 3))
-    lc_features = torch.view(_23, [_18, _19, _20, _25, int(_26)])
-    return lc_features
-  def forward3(self: __torch__.NN.Local_CNN_Branch,
-    x: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _27 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _28 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _29 = int(C)
-    _30 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _31 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    input = torch.view(x, [-1, _30, _31, int(W)])
-    _32 = (upconv).forward3(input, )
-    _33 = ops.prim.NumToTensor(torch.size(_32, 2))
-    _34 = int(_33)
-    _35 = ops.prim.NumToTensor(torch.size(_32, 3))
-    lc_features = torch.view(_32, [_27, _28, _29, _34, int(_35)])
-    return lc_features
-  def forward4(self: __torch__.NN.Local_CNN_Branch,
-    x: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _36 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _37 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _38 = int(C)
-    _39 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _40 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    input = torch.view(x, [-1, _39, _40, int(W)])
-    _41 = (upconv).forward4(input, )
-    _42 = ops.prim.NumToTensor(torch.size(_41, 2))
-    _43 = int(_42)
-    _44 = ops.prim.NumToTensor(torch.size(_41, 3))
-    lc_features = torch.view(_41, [_36, _37, _38, _43, int(_44)])
-    return lc_features
-  def forward5(self: __torch__.NN.Local_CNN_Branch,
-    x: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _45 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _46 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _47 = int(C)
-    _48 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _49 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    input = torch.view(x, [-1, _48, _49, int(W)])
-    _50 = (upconv).forward5(input, )
-    _51 = ops.prim.NumToTensor(torch.size(_50, 2))
-    _52 = int(_51)
-    _53 = ops.prim.NumToTensor(torch.size(_50, 3))
-    lc_features = torch.view(_50, [_45, _46, _47, _52, int(_53)])
-    return lc_features
-  def forward6(self: __torch__.NN.Local_CNN_Branch,
-    x: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _54 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _55 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _56 = int(C)
-    _57 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _58 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    input = torch.view(x, [-1, _57, _58, int(W)])
-    _59 = (upconv).forward6(input, )
-    _60 = ops.prim.NumToTensor(torch.size(_59, 2))
-    _61 = int(_60)
-    _62 = ops.prim.NumToTensor(torch.size(_59, 3))
-    lc_features = torch.view(_59, [_54, _55, _56, _61, int(_62)])
-    return lc_features
diff --git a/xe_weights14/glonet_p2/code/__torch__/NN.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NN.py.debug_pkl
deleted file mode 100644
index 59883a5f19542fcb780f29c3ecde70f815c7039e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NN.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bf3cb68338f3162c474b577dfa3a88831edb6363b419a1a95e929c97f5227f9b
-size 13187
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO.py b/xe_weights14/glonet_p2/code/__torch__/NO.py
deleted file mode 100644
index 99fd63184ee59af1e0ba8bbfb863efadc8cfadb4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO.py
+++ /dev/null
@@ -1,1099 +0,0 @@
-class GF_Block(Module):
-  __parameters__ = ["pos_embed", ]
-  __buffers__ = []
-  pos_embed : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  patch_embed : __torch__.NO.PatchEmbed
-  pos_drop : __torch__.torch.nn.modules.dropout.Dropout
-  blks : __torch__.torch.nn.modules.container.ModuleList
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_522.LayerNorm
-  lproj : __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential
-  final_dropout : __torch__.torch.nn.modules.linear.___torch_mangle_528.Identity
-  def forward(self: __torch__.NO.GF_Block,
-    x_raw: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks0 = self.blks
-    _10 = getattr(blks0, "10")
-    blks1 = self.blks
-    _9 = getattr(blks1, "9")
-    blks2 = self.blks
-    _8 = getattr(blks2, "8")
-    blks3 = self.blks
-    _7 = getattr(blks3, "7")
-    blks4 = self.blks
-    _6 = getattr(blks4, "6")
-    blks5 = self.blks
-    _5 = getattr(blks5, "5")
-    blks6 = self.blks
-    _4 = getattr(blks6, "4")
-    blks7 = self.blks
-    _3 = getattr(blks7, "3")
-    blks8 = self.blks
-    _2 = getattr(blks8, "2")
-    blks9 = self.blks
-    _1 = getattr(blks9, "1")
-    blks10 = self.blks
-    _0 = getattr(blks10, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x_raw, 0))
-    _12 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x_raw, 1))
-    _13 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x_raw, 2))
-    _14 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x_raw, 3))
-    _15 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x_raw, 4))
-    _16 = int(W)
-    B0 = ops.prim.NumToTensor(torch.size(x_raw, 0))
-    T0 = ops.prim.NumToTensor(torch.size(x_raw, 1))
-    C0 = ops.prim.NumToTensor(torch.size(x_raw, 2))
-    _17 = int(C0)
-    H0 = ops.prim.NumToTensor(torch.size(x_raw, 3))
-    _18 = int(H0)
-    W0 = ops.prim.NumToTensor(torch.size(x_raw, 4))
-    _19 = int(W0)
-    _20 = [int(torch.mul(B0, T0)), _17, _18, _19]
-    x = torch.view(x_raw, _20)
-    input = torch.add((patch_embed).forward(x, ), pos_embed)
-    _21 = (_0).forward((pos_drop).forward(input, ), )
-    _22 = (_3).forward((_2).forward((_1).forward(_21, ), ), )
-    _23 = (_6).forward((_5).forward((_4).forward(_22, ), ), )
-    _24 = (_9).forward((_8).forward((_7).forward(_23, ), ), )
-    _25 = (_11).forward((_10).forward(_24, ), )
-    _26 = torch.transpose((norm).forward(_25, ), 1, 2)
-    x0 = torch.contiguous(_26)
-    input0 = torch.reshape(x0, [-1, 768, 42, 90])
-    _27 = (final_dropout).forward()
-    gf_features = torch.reshape((lproj).forward(input0, ), [_12, _13, _14, _15, _16])
-    return gf_features
-  def forward1(self: __torch__.NO.GF_Block,
-    x: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks11 = self.blks
-    _10 = getattr(blks11, "10")
-    blks12 = self.blks
-    _9 = getattr(blks12, "9")
-    blks13 = self.blks
-    _8 = getattr(blks13, "8")
-    blks14 = self.blks
-    _7 = getattr(blks14, "7")
-    blks15 = self.blks
-    _6 = getattr(blks15, "6")
-    blks16 = self.blks
-    _5 = getattr(blks16, "5")
-    blks17 = self.blks
-    _4 = getattr(blks17, "4")
-    blks18 = self.blks
-    _3 = getattr(blks18, "3")
-    blks19 = self.blks
-    _2 = getattr(blks19, "2")
-    blks20 = self.blks
-    _1 = getattr(blks20, "1")
-    blks21 = self.blks
-    _0 = getattr(blks21, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _16 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _17 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _18 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _19 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _20 = int(W)
-    B1 = ops.prim.NumToTensor(torch.size(x, 0))
-    T1 = ops.prim.NumToTensor(torch.size(x, 1))
-    C1 = ops.prim.NumToTensor(torch.size(x, 2))
-    _21 = int(C1)
-    H1 = ops.prim.NumToTensor(torch.size(x, 3))
-    _22 = int(H1)
-    W1 = ops.prim.NumToTensor(torch.size(x, 4))
-    _23 = int(W1)
-    _24 = [int(torch.mul(B1, T1)), _21, _22, _23]
-    x1 = torch.view(x, _24)
-    input = torch.add((patch_embed).forward1(x1, ), pos_embed)
-    _25 = (_0).forward1((pos_drop).forward1(input, ), )
-    _26 = (_2).forward1((_1).forward1(_25, ), )
-    _27 = (_4).forward1((_3).forward1(_26, ), )
-    _28 = (_6).forward1((_5).forward1(_27, ), )
-    _29 = (_8).forward1((_7).forward1(_28, ), )
-    _30 = (_10).forward1((_9).forward1(_29, ), )
-    _31 = (norm).forward1((_11).forward1(_30, ), )
-    x2 = torch.contiguous(torch.transpose(_31, 1, 2))
-    input1 = torch.reshape(x2, [-1, 768, 42, 90])
-    _32 = (final_dropout).forward1()
-    gf_features = torch.reshape((lproj).forward1(input1, ), [_16, _17, _18, _19, _20])
-    return gf_features
-  def forward2(self: __torch__.NO.GF_Block,
-    x: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks22 = self.blks
-    _10 = getattr(blks22, "10")
-    blks23 = self.blks
-    _9 = getattr(blks23, "9")
-    blks24 = self.blks
-    _8 = getattr(blks24, "8")
-    blks25 = self.blks
-    _7 = getattr(blks25, "7")
-    blks26 = self.blks
-    _6 = getattr(blks26, "6")
-    blks27 = self.blks
-    _5 = getattr(blks27, "5")
-    blks28 = self.blks
-    _4 = getattr(blks28, "4")
-    blks29 = self.blks
-    _3 = getattr(blks29, "3")
-    blks30 = self.blks
-    _2 = getattr(blks30, "2")
-    blks31 = self.blks
-    _1 = getattr(blks31, "1")
-    blks32 = self.blks
-    _0 = getattr(blks32, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _33 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _34 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _35 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _36 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _37 = int(W)
-    B2 = ops.prim.NumToTensor(torch.size(x, 0))
-    T2 = ops.prim.NumToTensor(torch.size(x, 1))
-    C2 = ops.prim.NumToTensor(torch.size(x, 2))
-    _38 = int(C2)
-    H2 = ops.prim.NumToTensor(torch.size(x, 3))
-    _39 = int(H2)
-    W2 = ops.prim.NumToTensor(torch.size(x, 4))
-    _40 = int(W2)
-    _41 = [int(torch.mul(B2, T2)), _38, _39, _40]
-    x3 = torch.view(x, _41)
-    input = torch.add((patch_embed).forward2(x3, ), pos_embed)
-    _42 = (_0).forward2((pos_drop).forward2(input, ), )
-    _43 = (_2).forward2((_1).forward2(_42, ), )
-    _44 = (_4).forward2((_3).forward2(_43, ), )
-    _45 = (_6).forward2((_5).forward2(_44, ), )
-    _46 = (_8).forward2((_7).forward2(_45, ), )
-    _47 = (_10).forward2((_9).forward2(_46, ), )
-    _48 = (norm).forward2((_11).forward2(_47, ), )
-    x4 = torch.contiguous(torch.transpose(_48, 1, 2))
-    input2 = torch.reshape(x4, [-1, 768, 42, 90])
-    _49 = (final_dropout).forward2()
-    gf_features = torch.reshape((lproj).forward2(input2, ), [_33, _34, _35, _36, _37])
-    return gf_features
-  def forward3(self: __torch__.NO.GF_Block,
-    x: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks33 = self.blks
-    _10 = getattr(blks33, "10")
-    blks34 = self.blks
-    _9 = getattr(blks34, "9")
-    blks35 = self.blks
-    _8 = getattr(blks35, "8")
-    blks36 = self.blks
-    _7 = getattr(blks36, "7")
-    blks37 = self.blks
-    _6 = getattr(blks37, "6")
-    blks38 = self.blks
-    _5 = getattr(blks38, "5")
-    blks39 = self.blks
-    _4 = getattr(blks39, "4")
-    blks40 = self.blks
-    _3 = getattr(blks40, "3")
-    blks41 = self.blks
-    _2 = getattr(blks41, "2")
-    blks42 = self.blks
-    _1 = getattr(blks42, "1")
-    blks43 = self.blks
-    _0 = getattr(blks43, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _50 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _51 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _52 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _53 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _54 = int(W)
-    B3 = ops.prim.NumToTensor(torch.size(x, 0))
-    T3 = ops.prim.NumToTensor(torch.size(x, 1))
-    C3 = ops.prim.NumToTensor(torch.size(x, 2))
-    _55 = int(C3)
-    H3 = ops.prim.NumToTensor(torch.size(x, 3))
-    _56 = int(H3)
-    W3 = ops.prim.NumToTensor(torch.size(x, 4))
-    _57 = int(W3)
-    _58 = [int(torch.mul(B3, T3)), _55, _56, _57]
-    x5 = torch.view(x, _58)
-    input = torch.add((patch_embed).forward3(x5, ), pos_embed)
-    _59 = (_0).forward3((pos_drop).forward3(input, ), )
-    _60 = (_2).forward3((_1).forward3(_59, ), )
-    _61 = (_4).forward3((_3).forward3(_60, ), )
-    _62 = (_6).forward3((_5).forward3(_61, ), )
-    _63 = (_8).forward3((_7).forward3(_62, ), )
-    _64 = (_10).forward3((_9).forward3(_63, ), )
-    _65 = (norm).forward3((_11).forward3(_64, ), )
-    x6 = torch.contiguous(torch.transpose(_65, 1, 2))
-    input3 = torch.reshape(x6, [-1, 768, 42, 90])
-    _66 = (final_dropout).forward3()
-    gf_features = torch.reshape((lproj).forward3(input3, ), [_50, _51, _52, _53, _54])
-    return gf_features
-  def forward4(self: __torch__.NO.GF_Block,
-    x: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks44 = self.blks
-    _10 = getattr(blks44, "10")
-    blks45 = self.blks
-    _9 = getattr(blks45, "9")
-    blks46 = self.blks
-    _8 = getattr(blks46, "8")
-    blks47 = self.blks
-    _7 = getattr(blks47, "7")
-    blks48 = self.blks
-    _6 = getattr(blks48, "6")
-    blks49 = self.blks
-    _5 = getattr(blks49, "5")
-    blks50 = self.blks
-    _4 = getattr(blks50, "4")
-    blks51 = self.blks
-    _3 = getattr(blks51, "3")
-    blks52 = self.blks
-    _2 = getattr(blks52, "2")
-    blks53 = self.blks
-    _1 = getattr(blks53, "1")
-    blks54 = self.blks
-    _0 = getattr(blks54, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _67 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _68 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _69 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _70 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _71 = int(W)
-    B4 = ops.prim.NumToTensor(torch.size(x, 0))
-    T4 = ops.prim.NumToTensor(torch.size(x, 1))
-    C4 = ops.prim.NumToTensor(torch.size(x, 2))
-    _72 = int(C4)
-    H4 = ops.prim.NumToTensor(torch.size(x, 3))
-    _73 = int(H4)
-    W4 = ops.prim.NumToTensor(torch.size(x, 4))
-    _74 = int(W4)
-    _75 = [int(torch.mul(B4, T4)), _72, _73, _74]
-    x7 = torch.view(x, _75)
-    input = torch.add((patch_embed).forward4(x7, ), pos_embed)
-    _76 = (_0).forward4((pos_drop).forward4(input, ), )
-    _77 = (_2).forward4((_1).forward4(_76, ), )
-    _78 = (_4).forward4((_3).forward4(_77, ), )
-    _79 = (_6).forward4((_5).forward4(_78, ), )
-    _80 = (_8).forward4((_7).forward4(_79, ), )
-    _81 = (_10).forward4((_9).forward4(_80, ), )
-    _82 = (norm).forward4((_11).forward4(_81, ), )
-    x8 = torch.contiguous(torch.transpose(_82, 1, 2))
-    input4 = torch.reshape(x8, [-1, 768, 42, 90])
-    _83 = (final_dropout).forward4()
-    gf_features = torch.reshape((lproj).forward4(input4, ), [_67, _68, _69, _70, _71])
-    return gf_features
-  def forward5(self: __torch__.NO.GF_Block,
-    x: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks55 = self.blks
-    _10 = getattr(blks55, "10")
-    blks56 = self.blks
-    _9 = getattr(blks56, "9")
-    blks57 = self.blks
-    _8 = getattr(blks57, "8")
-    blks58 = self.blks
-    _7 = getattr(blks58, "7")
-    blks59 = self.blks
-    _6 = getattr(blks59, "6")
-    blks60 = self.blks
-    _5 = getattr(blks60, "5")
-    blks61 = self.blks
-    _4 = getattr(blks61, "4")
-    blks62 = self.blks
-    _3 = getattr(blks62, "3")
-    blks63 = self.blks
-    _2 = getattr(blks63, "2")
-    blks64 = self.blks
-    _1 = getattr(blks64, "1")
-    blks65 = self.blks
-    _0 = getattr(blks65, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _84 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _85 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _86 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _87 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _88 = int(W)
-    B5 = ops.prim.NumToTensor(torch.size(x, 0))
-    T5 = ops.prim.NumToTensor(torch.size(x, 1))
-    C5 = ops.prim.NumToTensor(torch.size(x, 2))
-    _89 = int(C5)
-    H5 = ops.prim.NumToTensor(torch.size(x, 3))
-    _90 = int(H5)
-    W5 = ops.prim.NumToTensor(torch.size(x, 4))
-    _91 = int(W5)
-    _92 = [int(torch.mul(B5, T5)), _89, _90, _91]
-    x9 = torch.view(x, _92)
-    input = torch.add((patch_embed).forward5(x9, ), pos_embed)
-    _93 = (_0).forward5((pos_drop).forward5(input, ), )
-    _94 = (_2).forward5((_1).forward5(_93, ), )
-    _95 = (_4).forward5((_3).forward5(_94, ), )
-    _96 = (_6).forward5((_5).forward5(_95, ), )
-    _97 = (_8).forward5((_7).forward5(_96, ), )
-    _98 = (_10).forward5((_9).forward5(_97, ), )
-    _99 = (norm).forward5((_11).forward5(_98, ), )
-    x10 = torch.contiguous(torch.transpose(_99, 1, 2))
-    input5 = torch.reshape(x10, [-1, 768, 42, 90])
-    _100 = (final_dropout).forward5()
-    gf_features = torch.reshape((lproj).forward5(input5, ), [_84, _85, _86, _87, _88])
-    return gf_features
-  def forward6(self: __torch__.NO.GF_Block,
-    x: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks66 = self.blks
-    _10 = getattr(blks66, "10")
-    blks67 = self.blks
-    _9 = getattr(blks67, "9")
-    blks68 = self.blks
-    _8 = getattr(blks68, "8")
-    blks69 = self.blks
-    _7 = getattr(blks69, "7")
-    blks70 = self.blks
-    _6 = getattr(blks70, "6")
-    blks71 = self.blks
-    _5 = getattr(blks71, "5")
-    blks72 = self.blks
-    _4 = getattr(blks72, "4")
-    blks73 = self.blks
-    _3 = getattr(blks73, "3")
-    blks74 = self.blks
-    _2 = getattr(blks74, "2")
-    blks75 = self.blks
-    _1 = getattr(blks75, "1")
-    blks76 = self.blks
-    _0 = getattr(blks76, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _101 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _102 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _103 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _104 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _105 = int(W)
-    B6 = ops.prim.NumToTensor(torch.size(x, 0))
-    T6 = ops.prim.NumToTensor(torch.size(x, 1))
-    C6 = ops.prim.NumToTensor(torch.size(x, 2))
-    _106 = int(C6)
-    H6 = ops.prim.NumToTensor(torch.size(x, 3))
-    _107 = int(H6)
-    W6 = ops.prim.NumToTensor(torch.size(x, 4))
-    _108 = int(W6)
-    _109 = [int(torch.mul(B6, T6)), _106, _107, _108]
-    x11 = torch.view(x, _109)
-    input = torch.add((patch_embed).forward6(x11, ), pos_embed)
-    _110 = (_0).forward6((pos_drop).forward6(input, ), )
-    _111 = (_2).forward6((_1).forward6(_110, ), )
-    _112 = (_4).forward6((_3).forward6(_111, ), )
-    _113 = (_6).forward6((_5).forward6(_112, ), )
-    _114 = (_8).forward6((_7).forward6(_113, ), )
-    _115 = (_10).forward6((_9).forward6(_114, ), )
-    _116 = (norm).forward6((_11).forward6(_115, ), )
-    x12 = torch.contiguous(torch.transpose(_116, 1, 2))
-    input6 = torch.reshape(x12, [-1, 768, 42, 90])
-    _117 = (final_dropout).forward6()
-    gf_features = torch.reshape((lproj).forward6(input6, ), [_101, _102, _103, _104, _105])
-    return gf_features
-class PatchEmbed(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  projection : __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d
-  norm : __torch__.torch.nn.modules.linear.Identity
-  def forward(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _118 = torch.flatten((projection).forward(x, ), 2)
-    x13 = torch.contiguous(torch.transpose(_118, 1, 2))
-    _119 = (norm).forward()
-    return x13
-  def forward1(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _120 = torch.flatten((projection).forward1(x, ), 2)
-    x14 = torch.contiguous(torch.transpose(_120, 1, 2))
-    _121 = (norm).forward1()
-    return x14
-  def forward2(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _122 = torch.flatten((projection).forward2(x, ), 2)
-    x15 = torch.contiguous(torch.transpose(_122, 1, 2))
-    _123 = (norm).forward2()
-    return x15
-  def forward3(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _124 = torch.flatten((projection).forward3(x, ), 2)
-    x16 = torch.contiguous(torch.transpose(_124, 1, 2))
-    _125 = (norm).forward3()
-    return x16
-  def forward4(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _126 = torch.flatten((projection).forward4(x, ), 2)
-    x17 = torch.contiguous(torch.transpose(_126, 1, 2))
-    _127 = (norm).forward4()
-    return x17
-  def forward5(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _128 = torch.flatten((projection).forward5(x, ), 2)
-    x18 = torch.contiguous(torch.transpose(_128, 1, 2))
-    _129 = (norm).forward5()
-    return x18
-  def forward6(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _130 = torch.flatten((projection).forward6(x, ), 2)
-    x19 = torch.contiguous(torch.transpose(_130, 1, 2))
-    _131 = (norm).forward6()
-    return x19
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.LayerNorm
-  filter : __torch__.NO.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_342.Identity
-  attn : __torch__.NO.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_343.LayerNorm
-  mlp : __torch__.NO.Mlp
-  def forward(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _132 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _133 = (drop_path).forward()
-    input = torch.add(argument_1, _132)
-    _134 = (mlp).forward((normlayer2).forward(input, ), )
-    _135 = (drop_path).forward1()
-    return torch.add(input, _134)
-  def forward1(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _136 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _137 = (drop_path).forward2()
-    input = torch.add(argument_1, _136)
-    _138 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _139 = (drop_path).forward3()
-    return torch.add(input, _138)
-  def forward2(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _140 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _141 = (drop_path).forward4()
-    input = torch.add(argument_1, _140)
-    _142 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _143 = (drop_path).forward5()
-    return torch.add(input, _142)
-  def forward3(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _144 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _145 = (drop_path).forward6()
-    input = torch.add(argument_1, _144)
-    _146 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _147 = (drop_path).forward7()
-    return torch.add(input, _146)
-  def forward4(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _148 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _149 = (drop_path).forward8()
-    input = torch.add(argument_1, _148)
-    _150 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _151 = (drop_path).forward9()
-    return torch.add(input, _150)
-  def forward5(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _152 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _153 = (drop_path).forward10()
-    input = torch.add(argument_1, _152)
-    _154 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _155 = (drop_path).forward11()
-    return torch.add(input, _154)
-  def forward6(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _156 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _157 = (drop_path).forward12()
-    input = torch.add(argument_1, _156)
-    _158 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _159 = (drop_path).forward13()
-    return torch.add(input, _158)
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.ReLU
-  bias : __torch__.torch.nn.modules.conv.Conv1d
-  def forward(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _160 = int(B)
-    _161 = int(B)
-    _162 = int(B)
-    _163 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _164 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _165 = int(C)
-    _166 = int(C)
-    _167 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_167)
-    _168 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_168)
-    x = torch.reshape(argument_1, [_163, 42, 90, _166])
-    x20 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _169 = ops.prim.NumToTensor(torch.size(x20, 1))
-    _170 = int(_169)
-    _171 = ops.prim.NumToTensor(torch.size(x20, 2))
-    x21 = torch.reshape(x20, [_162, _170, int(_171), 2, 384])
-    _172 = [torch.real(x21), torch.select(w1, 0, 0)]
-    _173 = torch.einsum("...bd, bdk->...bk", _172)
-    _174 = [torch.imag(x21), torch.select(w1, 0, 1)]
-    _175 = torch.einsum("...bd, bdk->...bk", _174)
-    input7 = torch.add(torch.sub(_173, _175), torch.select(b1, 0, 0))
-    _176 = torch.relu_(input7)
-    _177 = [torch.real(x21), torch.select(w1, 0, 1)]
-    _178 = torch.einsum("...bd, bdk->...bk", _177)
-    _179 = [torch.imag(x21), torch.select(w1, 0, 0)]
-    _180 = torch.einsum("...bd, bdk->...bk", _179)
-    input8 = torch.add(torch.add(_178, _180), torch.select(b1, 0, 1))
-    _181 = torch.relu_(input8)
-    _182 = torch.einsum("...bd, bdk->...bk", [_176, torch.select(w2, 0, 0)])
-    _183 = torch.einsum("...bd, bdk->...bk", [_181, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_182, _183), torch.select(b2, 0, 0))
-    _184 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _185 = torch.einsum("...bd, bdk->...bk", [_181, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_184, _185), torch.select(b2, 0, 1))
-    x22 = torch.stack([x_real, x_imag], -1)
-    x23 = torch.view_as_complex(x22)
-    _186 = ops.prim.NumToTensor(torch.size(x23, 1))
-    _187 = int(_186)
-    _188 = ops.prim.NumToTensor(torch.size(x23, 2))
-    x24 = torch.reshape(x23, [_161, _187, int(_188), 768])
-    x25 = torch.fft_irfft2(x24, [42, 90], [1, 2], "ortho")
-    x26 = torch.reshape(x25, [_160, _164, _165])
-    return torch.add(x26, bias0)
-  def forward1(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _189 = int(B)
-    _190 = int(B)
-    _191 = int(B)
-    _192 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _193 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _194 = int(C)
-    _195 = int(C)
-    _196 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_196)
-    _197 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_197)
-    x = torch.reshape(argument_1, [_192, 42, 90, _195])
-    x27 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _198 = ops.prim.NumToTensor(torch.size(x27, 1))
-    _199 = int(_198)
-    _200 = ops.prim.NumToTensor(torch.size(x27, 2))
-    x28 = torch.reshape(x27, [_191, _199, int(_200), 2, 384])
-    _201 = [torch.real(x28), torch.select(w1, 0, 0)]
-    _202 = torch.einsum("...bd, bdk->...bk", _201)
-    _203 = [torch.imag(x28), torch.select(w1, 0, 1)]
-    _204 = torch.einsum("...bd, bdk->...bk", _203)
-    input9 = torch.add(torch.sub(_202, _204), torch.select(b1, 0, 0))
-    _205 = torch.relu_(input9)
-    _206 = [torch.real(x28), torch.select(w1, 0, 1)]
-    _207 = torch.einsum("...bd, bdk->...bk", _206)
-    _208 = [torch.imag(x28), torch.select(w1, 0, 0)]
-    _209 = torch.einsum("...bd, bdk->...bk", _208)
-    input10 = torch.add(torch.add(_207, _209), torch.select(b1, 0, 1))
-    _210 = torch.relu_(input10)
-    _211 = torch.einsum("...bd, bdk->...bk", [_205, torch.select(w2, 0, 0)])
-    _212 = torch.einsum("...bd, bdk->...bk", [_210, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_211, _212), torch.select(b2, 0, 0))
-    _213 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _214 = torch.einsum("...bd, bdk->...bk", [_210, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_213, _214), torch.select(b2, 0, 1))
-    x29 = torch.stack([x_real, x_imag], -1)
-    x30 = torch.view_as_complex(x29)
-    _215 = ops.prim.NumToTensor(torch.size(x30, 1))
-    _216 = int(_215)
-    _217 = ops.prim.NumToTensor(torch.size(x30, 2))
-    x31 = torch.reshape(x30, [_190, _216, int(_217), 768])
-    x32 = torch.fft_irfft2(x31, [42, 90], [1, 2], "ortho")
-    x33 = torch.reshape(x32, [_189, _193, _194])
-    return torch.add(x33, bias1)
-  def forward2(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _218 = int(B)
-    _219 = int(B)
-    _220 = int(B)
-    _221 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _222 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _223 = int(C)
-    _224 = int(C)
-    _225 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_225)
-    _226 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_226)
-    x = torch.reshape(argument_1, [_221, 42, 90, _224])
-    x34 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _227 = ops.prim.NumToTensor(torch.size(x34, 1))
-    _228 = int(_227)
-    _229 = ops.prim.NumToTensor(torch.size(x34, 2))
-    x35 = torch.reshape(x34, [_220, _228, int(_229), 2, 384])
-    _230 = [torch.real(x35), torch.select(w1, 0, 0)]
-    _231 = torch.einsum("...bd, bdk->...bk", _230)
-    _232 = [torch.imag(x35), torch.select(w1, 0, 1)]
-    _233 = torch.einsum("...bd, bdk->...bk", _232)
-    input11 = torch.add(torch.sub(_231, _233), torch.select(b1, 0, 0))
-    _234 = torch.relu_(input11)
-    _235 = [torch.real(x35), torch.select(w1, 0, 1)]
-    _236 = torch.einsum("...bd, bdk->...bk", _235)
-    _237 = [torch.imag(x35), torch.select(w1, 0, 0)]
-    _238 = torch.einsum("...bd, bdk->...bk", _237)
-    input12 = torch.add(torch.add(_236, _238), torch.select(b1, 0, 1))
-    _239 = torch.relu_(input12)
-    _240 = torch.einsum("...bd, bdk->...bk", [_234, torch.select(w2, 0, 0)])
-    _241 = torch.einsum("...bd, bdk->...bk", [_239, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_240, _241), torch.select(b2, 0, 0))
-    _242 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _243 = torch.einsum("...bd, bdk->...bk", [_239, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_242, _243), torch.select(b2, 0, 1))
-    x36 = torch.stack([x_real, x_imag], -1)
-    x37 = torch.view_as_complex(x36)
-    _244 = ops.prim.NumToTensor(torch.size(x37, 1))
-    _245 = int(_244)
-    _246 = ops.prim.NumToTensor(torch.size(x37, 2))
-    x38 = torch.reshape(x37, [_219, _245, int(_246), 768])
-    x39 = torch.fft_irfft2(x38, [42, 90], [1, 2], "ortho")
-    x40 = torch.reshape(x39, [_218, _222, _223])
-    return torch.add(x40, bias2)
-  def forward3(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _247 = int(B)
-    _248 = int(B)
-    _249 = int(B)
-    _250 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _251 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _252 = int(C)
-    _253 = int(C)
-    _254 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_254)
-    _255 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_255)
-    x = torch.reshape(argument_1, [_250, 42, 90, _253])
-    x41 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _256 = ops.prim.NumToTensor(torch.size(x41, 1))
-    _257 = int(_256)
-    _258 = ops.prim.NumToTensor(torch.size(x41, 2))
-    x42 = torch.reshape(x41, [_249, _257, int(_258), 2, 384])
-    _259 = [torch.real(x42), torch.select(w1, 0, 0)]
-    _260 = torch.einsum("...bd, bdk->...bk", _259)
-    _261 = [torch.imag(x42), torch.select(w1, 0, 1)]
-    _262 = torch.einsum("...bd, bdk->...bk", _261)
-    input13 = torch.add(torch.sub(_260, _262), torch.select(b1, 0, 0))
-    _263 = torch.relu_(input13)
-    _264 = [torch.real(x42), torch.select(w1, 0, 1)]
-    _265 = torch.einsum("...bd, bdk->...bk", _264)
-    _266 = [torch.imag(x42), torch.select(w1, 0, 0)]
-    _267 = torch.einsum("...bd, bdk->...bk", _266)
-    input14 = torch.add(torch.add(_265, _267), torch.select(b1, 0, 1))
-    _268 = torch.relu_(input14)
-    _269 = torch.einsum("...bd, bdk->...bk", [_263, torch.select(w2, 0, 0)])
-    _270 = torch.einsum("...bd, bdk->...bk", [_268, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_269, _270), torch.select(b2, 0, 0))
-    _271 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _272 = torch.einsum("...bd, bdk->...bk", [_268, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_271, _272), torch.select(b2, 0, 1))
-    x43 = torch.stack([x_real, x_imag], -1)
-    x44 = torch.view_as_complex(x43)
-    _273 = ops.prim.NumToTensor(torch.size(x44, 1))
-    _274 = int(_273)
-    _275 = ops.prim.NumToTensor(torch.size(x44, 2))
-    x45 = torch.reshape(x44, [_248, _274, int(_275), 768])
-    x46 = torch.fft_irfft2(x45, [42, 90], [1, 2], "ortho")
-    x47 = torch.reshape(x46, [_247, _251, _252])
-    return torch.add(x47, bias3)
-  def forward4(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _276 = int(B)
-    _277 = int(B)
-    _278 = int(B)
-    _279 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _280 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _281 = int(C)
-    _282 = int(C)
-    _283 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_283)
-    _284 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_284)
-    x = torch.reshape(argument_1, [_279, 42, 90, _282])
-    x48 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _285 = ops.prim.NumToTensor(torch.size(x48, 1))
-    _286 = int(_285)
-    _287 = ops.prim.NumToTensor(torch.size(x48, 2))
-    x49 = torch.reshape(x48, [_278, _286, int(_287), 2, 384])
-    _288 = [torch.real(x49), torch.select(w1, 0, 0)]
-    _289 = torch.einsum("...bd, bdk->...bk", _288)
-    _290 = [torch.imag(x49), torch.select(w1, 0, 1)]
-    _291 = torch.einsum("...bd, bdk->...bk", _290)
-    input15 = torch.add(torch.sub(_289, _291), torch.select(b1, 0, 0))
-    _292 = torch.relu_(input15)
-    _293 = [torch.real(x49), torch.select(w1, 0, 1)]
-    _294 = torch.einsum("...bd, bdk->...bk", _293)
-    _295 = [torch.imag(x49), torch.select(w1, 0, 0)]
-    _296 = torch.einsum("...bd, bdk->...bk", _295)
-    input16 = torch.add(torch.add(_294, _296), torch.select(b1, 0, 1))
-    _297 = torch.relu_(input16)
-    _298 = torch.einsum("...bd, bdk->...bk", [_292, torch.select(w2, 0, 0)])
-    _299 = torch.einsum("...bd, bdk->...bk", [_297, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_298, _299), torch.select(b2, 0, 0))
-    _300 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _301 = torch.einsum("...bd, bdk->...bk", [_297, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_300, _301), torch.select(b2, 0, 1))
-    x50 = torch.stack([x_real, x_imag], -1)
-    x51 = torch.view_as_complex(x50)
-    _302 = ops.prim.NumToTensor(torch.size(x51, 1))
-    _303 = int(_302)
-    _304 = ops.prim.NumToTensor(torch.size(x51, 2))
-    x52 = torch.reshape(x51, [_277, _303, int(_304), 768])
-    x53 = torch.fft_irfft2(x52, [42, 90], [1, 2], "ortho")
-    x54 = torch.reshape(x53, [_276, _280, _281])
-    return torch.add(x54, bias4)
-  def forward5(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _305 = int(B)
-    _306 = int(B)
-    _307 = int(B)
-    _308 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _309 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _310 = int(C)
-    _311 = int(C)
-    _312 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_312)
-    _313 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_313)
-    x = torch.reshape(argument_1, [_308, 42, 90, _311])
-    x55 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _314 = ops.prim.NumToTensor(torch.size(x55, 1))
-    _315 = int(_314)
-    _316 = ops.prim.NumToTensor(torch.size(x55, 2))
-    x56 = torch.reshape(x55, [_307, _315, int(_316), 2, 384])
-    _317 = [torch.real(x56), torch.select(w1, 0, 0)]
-    _318 = torch.einsum("...bd, bdk->...bk", _317)
-    _319 = [torch.imag(x56), torch.select(w1, 0, 1)]
-    _320 = torch.einsum("...bd, bdk->...bk", _319)
-    input17 = torch.add(torch.sub(_318, _320), torch.select(b1, 0, 0))
-    _321 = torch.relu_(input17)
-    _322 = [torch.real(x56), torch.select(w1, 0, 1)]
-    _323 = torch.einsum("...bd, bdk->...bk", _322)
-    _324 = [torch.imag(x56), torch.select(w1, 0, 0)]
-    _325 = torch.einsum("...bd, bdk->...bk", _324)
-    input18 = torch.add(torch.add(_323, _325), torch.select(b1, 0, 1))
-    _326 = torch.relu_(input18)
-    _327 = torch.einsum("...bd, bdk->...bk", [_321, torch.select(w2, 0, 0)])
-    _328 = torch.einsum("...bd, bdk->...bk", [_326, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_327, _328), torch.select(b2, 0, 0))
-    _329 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _330 = torch.einsum("...bd, bdk->...bk", [_326, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_329, _330), torch.select(b2, 0, 1))
-    x57 = torch.stack([x_real, x_imag], -1)
-    x58 = torch.view_as_complex(x57)
-    _331 = ops.prim.NumToTensor(torch.size(x58, 1))
-    _332 = int(_331)
-    _333 = ops.prim.NumToTensor(torch.size(x58, 2))
-    x59 = torch.reshape(x58, [_306, _332, int(_333), 768])
-    x60 = torch.fft_irfft2(x59, [42, 90], [1, 2], "ortho")
-    x61 = torch.reshape(x60, [_305, _309, _310])
-    return torch.add(x61, bias5)
-  def forward6(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _334 = int(B)
-    _335 = int(B)
-    _336 = int(B)
-    _337 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _338 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _339 = int(C)
-    _340 = int(C)
-    _341 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_341)
-    _342 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_342)
-    x = torch.reshape(argument_1, [_337, 42, 90, _340])
-    x62 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _343 = ops.prim.NumToTensor(torch.size(x62, 1))
-    _344 = int(_343)
-    _345 = ops.prim.NumToTensor(torch.size(x62, 2))
-    x63 = torch.reshape(x62, [_336, _344, int(_345), 2, 384])
-    _346 = [torch.real(x63), torch.select(w1, 0, 0)]
-    _347 = torch.einsum("...bd, bdk->...bk", _346)
-    _348 = [torch.imag(x63), torch.select(w1, 0, 1)]
-    _349 = torch.einsum("...bd, bdk->...bk", _348)
-    input19 = torch.add(torch.sub(_347, _349), torch.select(b1, 0, 0))
-    _350 = torch.relu_(input19)
-    _351 = [torch.real(x63), torch.select(w1, 0, 1)]
-    _352 = torch.einsum("...bd, bdk->...bk", _351)
-    _353 = [torch.imag(x63), torch.select(w1, 0, 0)]
-    _354 = torch.einsum("...bd, bdk->...bk", _353)
-    input20 = torch.add(torch.add(_352, _354), torch.select(b1, 0, 1))
-    _355 = torch.relu_(input20)
-    _356 = torch.einsum("...bd, bdk->...bk", [_350, torch.select(w2, 0, 0)])
-    _357 = torch.einsum("...bd, bdk->...bk", [_355, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_356, _357), torch.select(b2, 0, 0))
-    _358 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _359 = torch.einsum("...bd, bdk->...bk", [_355, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_358, _359), torch.select(b2, 0, 1))
-    x64 = torch.stack([x_real, x_imag], -1)
-    x65 = torch.view_as_complex(x64)
-    _360 = ops.prim.NumToTensor(torch.size(x65, 1))
-    _361 = int(_360)
-    _362 = ops.prim.NumToTensor(torch.size(x65, 2))
-    x66 = torch.reshape(x65, [_335, _361, int(_362), 768])
-    x67 = torch.fft_irfft2(x66, [42, 90], [1, 2], "ortho")
-    x68 = torch.reshape(x67, [_334, _338, _339])
-    return torch.add(x68, bias6)
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.MultiheadAttention
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.Linear
-  act : __torch__.torch.nn.modules.activation.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_344.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout
-  def forward(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _363 = (act).forward((fc1).forward(argument_1, ), )
-    _364 = (fc3).forward((drop).forward(_363, ), )
-    return (drop).forward1(_364, )
-  def forward1(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _365 = (act).forward1((fc1).forward1(argument_1, ), )
-    _366 = (fc3).forward1((drop).forward2(_365, ), )
-    return (drop).forward3(_366, )
-  def forward2(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _367 = (act).forward2((fc1).forward2(argument_1, ), )
-    _368 = (fc3).forward2((drop).forward4(_367, ), )
-    return (drop).forward5(_368, )
-  def forward3(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _369 = (act).forward3((fc1).forward3(argument_1, ), )
-    _370 = (fc3).forward3((drop).forward6(_369, ), )
-    return (drop).forward7(_370, )
-  def forward4(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _371 = (act).forward4((fc1).forward4(argument_1, ), )
-    _372 = (fc3).forward4((drop).forward8(_371, ), )
-    return (drop).forward9(_372, )
-  def forward5(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _373 = (act).forward5((fc1).forward5(argument_1, ), )
-    _374 = (fc3).forward5((drop).forward10(_373, ), )
-    return (drop).forward11(_374, )
-  def forward6(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _375 = (act).forward6((fc1).forward6(argument_1, ), )
-    _376 = (fc3).forward6((drop).forward12(_375, ), )
-    return (drop).forward13(_376, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO.py.debug_pkl
deleted file mode 100644
index fe6982fd6d3e03db474397eb1565b62ae2b6d2a3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e60c9387ad370045f484ac96ebadf797b75599d5a0527e7fc6ffe05b48f89632
-size 119236
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_349.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_349.py
deleted file mode 100644
index 93427a5161038fcdbc7d78354cd03602e4b8a99d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_349.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_347.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_349.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_349.py.debug_pkl
deleted file mode 100644
index cb5bf78c557e5c88f21ba0b32dad6ea4311cf2e5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_349.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3f95e64212642c53a61166c59aefec11fcf065b5efb843bb7a8b9308a221388f
-size 89581
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_353.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_353.py
deleted file mode 100644
index 4970ec7aaec88019af2a220896ec7db6ab279429..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_353.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_352.MultiheadAttention
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_353.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_353.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_353.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_360.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_360.py
deleted file mode 100644
index 0d253a2f5198753f821e197454215830e3fb3c51..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_360.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_355.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_356.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_357.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_358.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_360.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_360.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_360.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_361.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_361.py
deleted file mode 100644
index df0b8b35129fc3b09e96344e009cd7145881f9f4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_361.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_346.LayerNorm
-  filter : __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_350.Identity
-  attn : __torch__.NO.___torch_mangle_353.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_360.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_361.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_361.py.debug_pkl
deleted file mode 100644
index fd6702cbd50ca163eb5e8f6c7a2b69fc597bdc6b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_361.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4390207bd71a2aece8f28c0b8e900aaf7d7e7beffddcde1ae0d1c0d1c3d9c015
-size 2912
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_365.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_365.py
deleted file mode 100644
index ac9b00c225cb8b3468dda543a4831cadf007f3d6..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_365.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_363.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_365.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_365.py.debug_pkl
deleted file mode 100644
index a5736b7500b4ca68d8c1848d70663c0d9072f98b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_365.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5f5017bf23f8b857246d09488f764736464d40274df71915b407ca438a911698
-size 89581
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_369.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_369.py
deleted file mode 100644
index ea354395ac35858261c9e45247bc69ea94c9213b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_369.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_368.MultiheadAttention
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_369.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_369.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_369.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_376.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_376.py
deleted file mode 100644
index 8cd56e06836952c6856c4f4a43549a8d788f46f1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_376.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_371.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_372.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_373.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_374.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_376.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_376.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_376.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_377.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_377.py
deleted file mode 100644
index 5c593e4f98e785e3696f76edb033833e9986b118..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_377.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm
-  filter : __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_366.Identity
-  attn : __torch__.NO.___torch_mangle_369.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_370.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_376.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_377.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_377.py.debug_pkl
deleted file mode 100644
index 955ab77875c4bfa7518cd68bc7029f3c009353dd..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_377.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ce90239864240fd9330b3eded13a3ce421e0793dc7ce49d731169cde19270c2d
-size 2912
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_381.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_381.py
deleted file mode 100644
index 94081617ebe8ce2efb23057ee3eada9555066e80..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_381.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_379.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_381.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_381.py.debug_pkl
deleted file mode 100644
index 6336740472980e0129a32fd76ab9d493bacfef9c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_381.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0dd1e11707dccb594d70fe8f4ca2909326afdc04606c31a2db70d474cb7d99e3
-size 89581
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_385.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_385.py
deleted file mode 100644
index c918d30932782cc1a86302c5e88f17395e5e5997..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_385.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_384.MultiheadAttention
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_385.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_385.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_385.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_392.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_392.py
deleted file mode 100644
index 3426c49e4cfc54889c0218f200604e9e6a7cdf84..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_392.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_387.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_389.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_390.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_391.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_392.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_392.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_392.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_393.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_393.py
deleted file mode 100644
index 2fe2a4687953b11ec99a9716f910b3f0d5856136..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_393.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_378.LayerNorm
-  filter : __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_382.Identity
-  attn : __torch__.NO.___torch_mangle_385.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_386.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_392.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_393.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_393.py.debug_pkl
deleted file mode 100644
index de47739ea381ead7424ac641283304d126ef296f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_393.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:715ad61a0ad8fb23ef1626c333b407293e68b3889bc4308ca63f8c48f6613c2a
-size 2912
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_397.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_397.py
deleted file mode 100644
index 7aa84e6a746b76a7fba35773a087f294c5d1ce27..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_397.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_395.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_397.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_397.py.debug_pkl
deleted file mode 100644
index 1a181e77e6042b5ee369488ad11717b28717ba73..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_397.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ab20e1e1e3b6418fa6782ca26e4db3a0e10108655b8b04fe3af3665ebf4d2ae6
-size 89581
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_401.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_401.py
deleted file mode 100644
index 0c84cadde5817b0812df4b5d4f9307504dab8f97..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_401.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_400.MultiheadAttention
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_401.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_401.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_401.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_408.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_408.py
deleted file mode 100644
index 9506beecb0dc8eb1368432a2b68400347bec393e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_408.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_403.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_404.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_405.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_408.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_408.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_408.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_409.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_409.py
deleted file mode 100644
index 108724813bef0bbdae726138a3de917d9406dfbf..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_409.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_394.LayerNorm
-  filter : __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity
-  attn : __torch__.NO.___torch_mangle_401.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_408.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_409.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_409.py.debug_pkl
deleted file mode 100644
index 6694b5ab39d821c94fd90aeab0cd9053758d4c15..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_409.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bbfe088aa03a928174f9644ea7bbe8fa7119352786fbc54f09c2317836a8cc12
-size 2912
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_413.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_413.py
deleted file mode 100644
index 4f40234c852a09b04df4e4f647aca080e43c76d7..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_413.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_411.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_413.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_413.py.debug_pkl
deleted file mode 100644
index 7b4e15a135341eb0757cf5528585a8651f245a75..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_413.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:04e95a449041e19a8b7cc4d2b174ea51515544a54c46df1d00b74bffb7ae69fe
-size 89581
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_417.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_417.py
deleted file mode 100644
index 8e065aab89d3135e5450a460a849a4659cf9d427..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_417.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_416.MultiheadAttention
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_417.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_417.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_417.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_424.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_424.py
deleted file mode 100644
index 6af7f16c1641c27928bcea46b483ba498d9d169d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_424.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_420.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_421.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_424.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_424.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_424.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_425.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_425.py
deleted file mode 100644
index e45c3f8e05f9272c3aa3815af13a9f9c1c9e1aaf..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_425.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm
-  filter : __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity
-  attn : __torch__.NO.___torch_mangle_417.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_418.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_424.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_425.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_425.py.debug_pkl
deleted file mode 100644
index 629a1b72ab210979ba6ee41e4bfb2e4120400b5a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_425.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2b10f06bdd8bbc07e11f3236a85d850b963591311f804838d2370a08d9f7b0f4
-size 2912
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_429.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_429.py
deleted file mode 100644
index b7c735ad9940ac3f25c60ccae0250c587b86bda2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_429.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_427.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_429.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_429.py.debug_pkl
deleted file mode 100644
index 82acd2fdc4f07bb581e831c533861ef067c4bcd2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_429.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:05ddfc5a8adf5cc4784bdbc40c06c0bf6a20977ab75c5df0433162e3c1c42ca9
-size 89581
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_433.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_433.py
deleted file mode 100644
index f4546a26b39d44e6215e6a1c45236858ff41606e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_433.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_432.MultiheadAttention
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_433.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_433.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_433.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_440.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_440.py
deleted file mode 100644
index cabcaf66e8d427da2b7950fb2a7617a27e04426e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_440.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_435.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_436.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_437.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_438.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_440.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_440.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_440.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_441.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_441.py
deleted file mode 100644
index a2da189f77e0e1617dcb1aa637a403fc569985d4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_441.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_426.LayerNorm
-  filter : __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity
-  attn : __torch__.NO.___torch_mangle_433.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_434.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_440.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_441.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_441.py.debug_pkl
deleted file mode 100644
index ef45d9d185f5f5a091c101b438d04d3fb23227b3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_441.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:22d4756d4a83df379bb923be756ee183cf5875d3041a9b87b19a210dd512dc73
-size 2912
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_445.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_445.py
deleted file mode 100644
index 6a9dac11b3289499072ad8ccbbf0495b9eea8e1a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_445.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_443.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_445.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_445.py.debug_pkl
deleted file mode 100644
index cabe0af10d67324a8452b1f884232e4d17b18241..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_445.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ffcc15f35c160efdb871feece05917324f4ac968287c0a8293c121bae4c3d73f
-size 89581
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_449.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_449.py
deleted file mode 100644
index 2432a75247f8a0da5f9216e39c65b0a9cd5be3fe..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_449.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_448.MultiheadAttention
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_449.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_449.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_449.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_456.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_456.py
deleted file mode 100644
index 6843eb7c1a066c02cf3a6e938d16fa42e8bb6e45..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_456.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_451.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_452.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_453.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_454.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_456.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_456.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_456.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_457.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_457.py
deleted file mode 100644
index c55e7606dfe69502e136f7147e5d77e076ad281e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_457.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_442.LayerNorm
-  filter : __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_446.Identity
-  attn : __torch__.NO.___torch_mangle_449.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_450.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_456.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_457.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_457.py.debug_pkl
deleted file mode 100644
index 7c390f982152e5d3fac9ff2c3c784b8d6a21f6d3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_457.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:45eb23a99c72d2f09f4623a98813c091713626401f8a680ba2b2eb0b1d67342d
-size 2912
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_461.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_461.py
deleted file mode 100644
index 1ecafd2a33d2736efc15e5bc423fb54f8aca80a4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_461.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_459.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_461.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_461.py.debug_pkl
deleted file mode 100644
index 52944141732c4bc7f12ac43176e3cdc32c3e4ad2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_461.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c8b9b0e8b70196d6f75dd2a577e4cd9e7c7b8bacd1581bc5d281b9d0d6f8d168
-size 89581
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_465.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_465.py
deleted file mode 100644
index 51026ab5508fc30ede9740277a8936d411133e9b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_465.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_464.MultiheadAttention
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_465.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_465.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_465.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_472.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_472.py
deleted file mode 100644
index f8c29dbe815fed9f8aac611ba8dd03c5ec883f5f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_472.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_467.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_468.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_469.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_470.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_471.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_472.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_472.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_472.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_473.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_473.py
deleted file mode 100644
index 3f8c24f9b3c4411c54b346cf5be73133e0c2255c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_473.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_458.LayerNorm
-  filter : __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_462.Identity
-  attn : __torch__.NO.___torch_mangle_465.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_466.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_472.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_473.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_473.py.debug_pkl
deleted file mode 100644
index 6f25d2046333c1ed621b2f1ef67a0c2e1fe75c21..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_473.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:962deb87cb2bc5815b4a10a4f3a499a815c0775bea52a54498f9964bd7ede240
-size 2912
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_477.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_477.py
deleted file mode 100644
index 814cbc85a36c97da1255b60c32598539be4faad9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_477.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_475.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_477.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_477.py.debug_pkl
deleted file mode 100644
index 1564f1425038852ae8969427c83f2b62e9184a68..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_477.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9039afbc26377a639bd53e84bc62c923287efcdff23bd7d22bd24706545446af
-size 89581
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_481.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_481.py
deleted file mode 100644
index b7875f2381497446d1385d7a7c6f569e6a67e0e1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_481.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_480.MultiheadAttention
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_481.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_481.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_481.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_488.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_488.py
deleted file mode 100644
index 9258f7a6d0eaed128a862b0d81a8e28711e13a02..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_488.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_483.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_484.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_485.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_486.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_487.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_488.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_488.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_488.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_489.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_489.py
deleted file mode 100644
index e5e30c6bab14c47d0128ecefabfe5758949de3e3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_489.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_474.LayerNorm
-  filter : __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_478.Identity
-  attn : __torch__.NO.___torch_mangle_481.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_482.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_488.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_489.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_489.py.debug_pkl
deleted file mode 100644
index c0d467125f9e640407f6393ee0fc9fac3b86481d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_489.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:86a86c0c8adb987c885ad6c2b06c6206ff76c4fe2444d148d002529563be0ee2
-size 2912
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_493.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_493.py
deleted file mode 100644
index 99b86c5edabff184ce203226f10c256c6b43bc3b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_493.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_491.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_493.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_493.py.debug_pkl
deleted file mode 100644
index 0a1cf8306d8d1655d2a5e5709c74c82f634888a0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_493.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bf4fff601b0c916a2d7eee9d65d940a37ba66e188d0490714bf01905ffbc375a
-size 89581
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_497.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_497.py
deleted file mode 100644
index f2c0b0958e7c681905129bc68a122fd21adb7dd9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_497.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_496.MultiheadAttention
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_497.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_497.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_497.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_504.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_504.py
deleted file mode 100644
index 2ce55548d3471efd665e2fdc94091e2d9b43190e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_504.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_499.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_501.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_502.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_503.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_504.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_504.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_504.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_505.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_505.py
deleted file mode 100644
index 2f82135d17cd39c5ffedb478e96a8649283e134e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_505.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_490.LayerNorm
-  filter : __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity
-  attn : __torch__.NO.___torch_mangle_497.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_498.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_504.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_505.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_505.py.debug_pkl
deleted file mode 100644
index 0349da743d34259d1d402eb36117bcf471291d64..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_505.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1eda09eb523b97df04c708ae3af5e5a72c64f7580ffd1cea3fdc202d7aadc4b2
-size 2912
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_509.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_509.py
deleted file mode 100644
index 422e701240a1c1c577b8210dbb8843434f408877..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_509.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_507.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_509.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_509.py.debug_pkl
deleted file mode 100644
index 4da278f1b3a8e638b36b2d824d56133d340725af..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_509.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c6a5e28a0da443fddc4d91dea5ada184074630a57c28fe01fbda1192b679b4a9
-size 89581
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_513.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_513.py
deleted file mode 100644
index e343f5408feb0e565cfc9d80fadd35d75a450aae..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_513.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_512.MultiheadAttention
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_513.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_513.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_513.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_520.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_520.py
deleted file mode 100644
index c0e9f3b6e0810ddce1199b2f8e2b78177a7da1e5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_520.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_515.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_517.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_518.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_519.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_520.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_520.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_520.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_521.py b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_521.py
deleted file mode 100644
index dc7c9b758b44e1b522d788691f24c9bc49a3968b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_521.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_506.LayerNorm
-  filter : __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_510.Identity
-  attn : __torch__.NO.___torch_mangle_513.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_514.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_520.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_521.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_521.py.debug_pkl
deleted file mode 100644
index 4956e5e6b3c59fa87ed2005f808f1fe4834a839d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/NO/___torch_mangle_521.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d3515eb6708ba414fdbb569dd05e1f25b609e638fc9dac656236cb3ec1f79f67
-size 2912
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock.py b/xe_weights14/glonet_p2/code/__torch__/dblock.py
deleted file mode 100644
index 17b7bcea8e379b9efe2204d2834439fa35070bcb..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock.py
+++ /dev/null
@@ -1,160 +0,0 @@
-class TeDev(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_541.LayerNorm
-  enc : __torch__.torch.nn.modules.container.___torch_mangle_692.Sequential
-  blocks : __torch__.torch.nn.modules.container.___torch_mangle_846.ModuleList
-  dec : __torch__.torch.nn.modules.container.___torch_mangle_999.Sequential
-  def forward(self: __torch__.dblock.TeDev,
-    x: Tensor) -> Tensor:
-    dec = self.dec
-    _7 = getattr(dec, "7")
-    dec0 = self.dec
-    _6 = getattr(dec0, "6")
-    dec1 = self.dec
-    _5 = getattr(dec1, "5")
-    dec2 = self.dec
-    _4 = getattr(dec2, "4")
-    dec3 = self.dec
-    _3 = getattr(dec3, "3")
-    dec4 = self.dec
-    _2 = getattr(dec4, "2")
-    dec5 = self.dec
-    _1 = getattr(dec5, "1")
-    dec6 = self.dec
-    _0 = getattr(dec6, "0")
-    norm = self.norm
-    blocks = self.blocks
-    _11 = getattr(blocks, "11")
-    blocks0 = self.blocks
-    _10 = getattr(blocks0, "10")
-    blocks1 = self.blocks
-    _9 = getattr(blocks1, "9")
-    blocks2 = self.blocks
-    _8 = getattr(blocks2, "8")
-    blocks3 = self.blocks
-    _70 = getattr(blocks3, "7")
-    blocks4 = self.blocks
-    _60 = getattr(blocks4, "6")
-    blocks5 = self.blocks
-    _50 = getattr(blocks5, "5")
-    blocks6 = self.blocks
-    _40 = getattr(blocks6, "4")
-    blocks7 = self.blocks
-    _30 = getattr(blocks7, "3")
-    blocks8 = self.blocks
-    _20 = getattr(blocks8, "2")
-    blocks9 = self.blocks
-    _12 = getattr(blocks9, "1")
-    blocks10 = self.blocks
-    _00 = getattr(blocks10, "0")
-    enc = self.enc
-    _71 = getattr(enc, "7")
-    enc0 = self.enc
-    _61 = getattr(enc0, "6")
-    enc1 = self.enc
-    _51 = getattr(enc1, "5")
-    enc2 = self.enc
-    _41 = getattr(enc2, "4")
-    enc3 = self.enc
-    _31 = getattr(enc3, "3")
-    enc4 = self.enc
-    _21 = getattr(enc4, "2")
-    enc5 = self.enc
-    _13 = getattr(enc5, "1")
-    enc6 = self.enc
-    _01 = getattr(enc6, "0")
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _14 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _15 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _16 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _17 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _18 = int(W)
-    _19 = [_14, int(torch.mul(T, C)), _17, _18]
-    input = torch.reshape(x, _19)
-    _22 = (_01).forward(input, )
-    _23 = (_13).forward(_22, )
-    _24 = (_21).forward(_23, )
-    _25 = (_31).forward(_24, )
-    _26 = (_41).forward(_25, )
-    _27 = (_51).forward(_26, )
-    _28 = (_61).forward(_27, )
-    _29 = (_71).forward(_28, )
-    B0 = ops.prim.NumToTensor(torch.size(_29, 0))
-    _32 = int(B0)
-    _33 = int(B0)
-    _34 = int(B0)
-    D = ops.prim.NumToTensor(torch.size(_29, 1))
-    _35 = int(D)
-    _36 = int(D)
-    H0 = ops.prim.NumToTensor(torch.size(_29, 2))
-    _37 = int(H0)
-    _38 = int(H0)
-    W0 = ops.prim.NumToTensor(torch.size(_29, 3))
-    _39 = int(W0)
-    _42 = int(W0)
-    N = torch.mul(H0, W0)
-    _43 = int(N)
-    z = torch.contiguous(torch.permute(_29, [0, 2, 3, 1]))
-    input0 = torch.view(z, [_34, _43, _36])
-    _44 = (_12).forward((_00).forward(input0, ), )
-    _45 = (_30).forward((_20).forward(_44, ), )
-    _46 = (_50).forward((_40).forward(_45, ), )
-    _47 = (_70).forward((_60).forward(_46, ), )
-    _48 = (_10).forward((_9).forward((_8).forward(_47, ), ), )
-    _49 = (norm).forward((_11).forward(_48, ), )
-    z0 = torch.contiguous(torch.permute(_49, [0, 2, 1]))
-    input1 = torch.reshape(z0, [_33, _35, _38, _42])
-    input2 = torch.cat([(_0).forward(input1, ), _28], 1)
-    input3 = torch.cat([(_1).forward(input2, ), _27], 1)
-    input4 = torch.cat([(_2).forward(input3, ), _26], 1)
-    input5 = torch.cat([(_3).forward(input4, ), _25], 1)
-    input6 = torch.cat([(_4).forward(input5, ), _24], 1)
-    input7 = torch.cat([(_5).forward(input6, ), _23], 1)
-    input8 = torch.cat([(_6).forward(input7, ), _22], 1)
-    y = torch.reshape((_7).forward(input8, ), [_32, _15, _16, _37, _39])
-    return torch.add(y, x)
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_542.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_558.Sequential
-  def forward(self: __torch__.dblock.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _30 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_30, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_30, ))
-    y1 = torch.add_(y0, (_2).forward(_30, ))
-    return torch.add_(y1, (_3).forward(_30, ))
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_543.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_544.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_545.LeakyReLU
-  def forward(self: __torch__.dblock.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _31 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_31, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock.py.debug_pkl
deleted file mode 100644
index 2c9fe0234d4e94c8e6df86a9142ea04e8d5cf1f1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d8f8a48ebe67ebde836d1b69642e07db42fee9874c6fd5aadf6dfd47c92ee9ae
-size 6825
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_549.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_549.py
deleted file mode 100644
index 0926398c6f28a7475e4ff8016ca5554166e16639..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_549.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_546.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_547.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_548.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_549.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_549.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_549.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_549.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_553.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_553.py
deleted file mode 100644
index 8f9ba243b10b7efaba9b158d3076528e14ffc303..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_553.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_550.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_551.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_552.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_553.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_553.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_553.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_553.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_557.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_557.py
deleted file mode 100644
index c9638ad03090a62cdb9b76a1df7ed0e56135d11a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_557.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_554.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_555.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_556.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_557.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_557.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_557.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_557.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_563.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_563.py
deleted file mode 100644
index 14f10363b3ff5ffed26868ab0af139bef8af4f0a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_563.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_560.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_561.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_562.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_563.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_563.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_563.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_563.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_567.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_567.py
deleted file mode 100644
index 5a87dd1c12757d9cb1da748ec7e6ff22788d18d4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_567.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_564.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_565.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_566.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_567.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_567.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_567.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_567.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_571.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_571.py
deleted file mode 100644
index f1a8c968331a80cc08230816c10cd3476f1429d2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_571.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_568.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_569.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_570.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_571.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_571.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_571.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_571.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_575.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_575.py
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index afb49821abda9f44f21c8b0224d54a548f7df894..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_575.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_572.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_573.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_574.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_575.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_575.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_575.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_575.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_577.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_577.py
deleted file mode 100644
index b308374844388faba3c28fa7b6e7b4925de74ed4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_577.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_559.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_576.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_577.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_577.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_577.py.debug_pkl
deleted file mode 100644
index 552970120b5264098a1c1c3c02378661e470be76..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_577.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_582.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_582.py
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index 98d4879266bdf52adfb68ed65c7f9dfd5ffc4958..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_582.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_579.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_580.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_581.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_582.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_582.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_582.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_582.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_586.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_586.py
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index 0af0c27e64b64f1b31bfbb5de58fadd6f743cd91..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_586.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_583.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_584.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_585.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_586.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_586.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_586.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_586.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_590.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_590.py
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index c7637fe96d2b6f5dad03bc54aa9fc27197ad58ac..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_590.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_587.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_588.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_589.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_590.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_590.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_590.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_590.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_594.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_594.py
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_594.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_592.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_593.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_594.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_594.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_594.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_596.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
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-  training : bool
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-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_578.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_595.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_596.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
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-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_596.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_596.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_601.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_601.py
deleted file mode 100644
index 8744a48bdadb3c440742da085de34a8261414ff6..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_601.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_598.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_599.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_600.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_601.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_601.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_601.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_601.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_605.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_605.py
deleted file mode 100644
index 63a516a8223f79f765464019bb087049424e66b6..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_605.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_602.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_603.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_604.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_605.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_605.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_605.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_605.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_609.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_606.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_607.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_608.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_609.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_609.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_609.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_609.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_613.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_610.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_611.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_612.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_613.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_613.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_613.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_613.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_615.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_615.py
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_615.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_597.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_614.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_615.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
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-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_615.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_615.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_615.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_620.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_620.py
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_620.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_617.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_618.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_619.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_620.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_620.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_620.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_620.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_624.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_621.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_622.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_623.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_624.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_624.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_624.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_624.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_628.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_625.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_626.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_627.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_628.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
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-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_628.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_628.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_632.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_629.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_630.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_631.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_632.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_632.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_632.py.debug_pkl
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+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_634.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_634.py
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_634.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_616.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_633.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_634.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
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-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_634.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_634.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_634.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_639.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_639.py
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_639.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_636.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_637.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_638.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_639.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_639.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_639.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_643.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_640.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_641.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_642.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_643.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_643.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_643.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_643.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_647.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_647.py
deleted file mode 100644
index a75fa208a32c1a96a8272af84f8f8c18ae7d1c28..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_647.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_644.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_645.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_646.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_647.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_647.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_647.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_647.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_651.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_651.py
deleted file mode 100644
index 316137893e25fb78704796847f453e2fff621f21..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_651.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_648.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_649.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_650.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_651.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_651.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_651.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_651.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_653.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_653.py
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index a13e7e64bcfc784bb5e0ec412e369883c73912e7..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_653.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_635.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_652.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_653.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_653.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_653.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_653.py.debug_pkl
+++ /dev/null
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_658.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_658.py
deleted file mode 100644
index 2b758362434abf83bbafe09d033de49e46ef98ef..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_658.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_655.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_656.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_657.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_658.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_658.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_658.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_658.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_662.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_662.py
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index 280fb2e1822d38d1c8e575a3dbaf5dd870b6d730..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_662.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_659.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_660.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_661.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_662.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_662.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_662.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_662.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_666.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_666.py
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_666.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_663.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_664.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_665.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_666.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_666.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_666.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_666.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_670.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_670.py
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_670.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_667.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_668.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_669.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_670.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_670.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_670.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_670.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_672.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_672.py
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index 0d15292de2dc1f5d3f1cb813f78f1c51532cc521..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_672.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_654.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_671.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_672.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
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-    lls2 = self.lls
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-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_672.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_672.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_672.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_677.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_677.py
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_677.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_674.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_675.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_676.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_677.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_677.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_677.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_677.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_681.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_678.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_679.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_680.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_681.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_681.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_681.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_685.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_682.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_683.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_684.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_685.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_685.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_685.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_689.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_686.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_687.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_688.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_689.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_689.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_689.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_689.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_691.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_691.py
deleted file mode 100644
index 4072e9b6786707376a3559efccad9e83727d0d86..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_691.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_673.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_690.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_691.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_691.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_691.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_691.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_851.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_851.py
deleted file mode 100644
index 96b36a3dfebc3573782e4935712ef6c53ef7bc46..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_851.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_848.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_849.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_850.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_851.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_851.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_851.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_851.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_855.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_855.py
deleted file mode 100644
index ee2e75d0ed1329ede6b8c0350de0d50e81cea2da..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_855.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_852.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_853.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_854.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_855.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_855.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_855.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_855.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_859.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_856.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_857.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_858.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_859.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_859.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_859.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_859.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_863.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_863.py
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_863.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_860.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_861.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_862.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_863.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_863.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_863.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_863.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_865.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_865.py
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index b2a1efb57079c24bedbd1736fa84ede97fb8c878..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_865.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_847.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_864.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_865.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_865.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_865.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_870.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_867.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_868.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_869.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_870.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_870.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_870.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_874.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_874.py
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_874.py
+++ /dev/null
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-class GroupConv2d(Module):
-  __parameters__ = []
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_871.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_872.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_873.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_874.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_874.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_874.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_874.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_878.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_875.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_876.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_877.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_878.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
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-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_878.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_878.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_882.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_882.py
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_882.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_879.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_880.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_881.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_882.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_882.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_882.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_884.py
+++ /dev/null
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-class Inception(Module):
-  __parameters__ = []
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-  training : bool
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-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_866.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_883.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_884.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
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-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_884.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_884.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_884.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_889.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_889.py
deleted file mode 100644
index 538e0ffe9bd70126a1ef16b86ed8a7c36bfd9425..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_889.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_886.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_887.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_888.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_889.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_889.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_889.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_889.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_893.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_893.py
deleted file mode 100644
index 22ff9a3c5602b0cc8e17b636ec73b6a8000146cc..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_893.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_890.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_891.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_892.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_893.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_893.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_893.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_893.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_897.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_897.py
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index b2f4f65dfe1133a30d581855d308bb7ec39ec279..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_897.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_894.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_895.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_896.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_897.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_897.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_897.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_897.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_901.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_901.py
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index ac540d7f8ec486e406af35314e033e6889aadc2b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_901.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_898.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_899.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_900.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_901.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_901.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_901.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_901.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_903.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_903.py
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index 30aec46015da8a2c4dd4bb4ada306845f2fe8f16..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_903.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_885.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_902.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_903.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_903.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_903.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_903.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_908.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_908.py
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index db29bd2eadd972e6b615fbe61008f3210539673f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_908.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_905.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_906.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_907.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_908.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_908.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_908.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_908.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_912.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_912.py
deleted file mode 100644
index b994569ff3a60346240199dbbc625411b97a6937..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_912.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_909.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_910.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_911.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_912.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_912.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_912.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_912.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_916.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_916.py
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index d23364439016107f3cc5bd8794ae5dfa9468afc1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_916.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_913.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_914.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_915.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_916.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_916.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_916.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_916.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_920.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_920.py
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_920.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_917.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_918.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_919.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_920.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_920.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_920.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_920.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_922.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_922.py
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index 1003cadd1dfe24822c28b1e726f6bc32af7fb21e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_922.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_904.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_921.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_922.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
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-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_922.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_922.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_922.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_927.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_927.py
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_927.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_924.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_925.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_926.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_927.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_927.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_927.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_927.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_931.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_931.py
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_931.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_928.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_929.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_930.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_931.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_931.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_931.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_931.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_935.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_935.py
deleted file mode 100644
index d643fe562c4486974e9b47ddd96233ca40f5ebc0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_935.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_932.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_933.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_934.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_935.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_935.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_935.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_935.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_939.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_939.py
deleted file mode 100644
index 1e29ddb51404745cd2eb652bf54168f36d5a5862..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_939.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_936.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_937.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_938.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_939.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_939.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_939.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_939.py.debug_pkl
+++ /dev/null
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_941.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_941.py
deleted file mode 100644
index 8bd7798b33bd9ab573c75fe03b7254ac647b429c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_941.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_923.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_940.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_941.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
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-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_941.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_941.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_946.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_946.py
deleted file mode 100644
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_946.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_943.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_944.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_945.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_946.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_946.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_946.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_950.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_950.py
deleted file mode 100644
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_950.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_947.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_948.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_949.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_950.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_950.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_950.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_950.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_954.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_954.py
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_954.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_951.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_952.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_953.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_954.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_954.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_954.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_958.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_955.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_956.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_957.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_958.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_958.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_958.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_960.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_960.py
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_960.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_942.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_959.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_960.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
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-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_960.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_960.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_965.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_965.py
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_965.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_962.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_963.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_964.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_965.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
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-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_965.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_965.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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-class GroupConv2d(Module):
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-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_968.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_969.GroupConv2d,
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-    activate = self.activate
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-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_969.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_969.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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+++ /dev/null
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-class GroupConv2d(Module):
-  __parameters__ = []
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-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_972.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_973.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
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-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_973.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_973.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_977.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_977.py
deleted file mode 100644
index f2e2d145fbf384f57002bafc0b9faacd45e4cf9c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_977.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_974.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_975.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_976.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_977.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_977.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_977.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_977.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_979.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_979.py
deleted file mode 100644
index 838ff06c98446f0dbcafb582d3d9664f3bd4b806..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_979.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_961.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_978.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_979.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_979.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_979.py.debug_pkl
deleted file mode 100644
index b3acd9c6928a70d75226621fa48efbadcd0ec4d2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_979.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_984.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_984.py
deleted file mode 100644
index ac2bf0004f91e9fbcde1882ef4f3e18dbc7cc344..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_984.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_981.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_982.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_983.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_984.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_984.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_984.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_984.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_988.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_988.py
deleted file mode 100644
index de72293c546e8a051a787687ce2eb4f7003559e6..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_988.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_985.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_986.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_987.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_988.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_988.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_988.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_988.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_992.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_992.py
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index 3a668cf2fe683c65a7ab0d85d5d9e006f9a85135..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_992.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_989.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_990.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_991.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_992.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_992.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_992.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_992.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_996.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_996.py
deleted file mode 100644
index 0fc7b031dfb8890175beff5b40570a464b2d5d2a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_996.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_993.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_994.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_995.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_996.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_996.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_996.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_996.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_998.py b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_998.py
deleted file mode 100644
index cba35dd15f504e961e33b5462b147e234737241f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_998.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_980.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_997.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_998.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_998.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_998.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p2/code/__torch__/dblock/___torch_mangle_998.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu.py b/xe_weights14/glonet_p2/code/__torch__/fcu.py
deleted file mode 100644
index b73075e7f2aaedb75cc7daec2932a8bdeee0c363..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu.py
+++ /dev/null
@@ -1,107 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_693.LayerNorm
-  filter : __torch__.fcu.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_696.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_697.LayerNorm
-  mlp : __torch__.fcu.Mlp
-  def forward(self: __torch__.fcu.FourierNetBlock,
-    input: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(input, ), )
-    _1 = (drop_path).forward()
-    input0 = torch.add(input, _0)
-    _2 = (mlp).forward((normlayer2).forward(input0, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input0, _2)
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_694.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_695.Conv1d
-  def forward(self: __torch__.fcu.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _4 = int(B)
-    _5 = int(B)
-    _6 = int(B)
-    _7 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _8 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _9 = int(C)
-    _10 = int(C)
-    _11 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_11)
-    _12 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_12)
-    x = torch.reshape(argument_1, [_7, 336, 720, _10])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _13 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _14 = int(_13)
-    _15 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_6, _14, int(_15), 2, 64])
-    _16 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _17 = torch.einsum("...bd, bdk->...bk", _16)
-    _18 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _19 = torch.einsum("...bd, bdk->...bk", _18)
-    input1 = torch.add(torch.sub(_17, _19), torch.select(b1, 0, 0))
-    _20 = torch.relu_(input1)
-    _21 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _22 = torch.einsum("...bd, bdk->...bk", _21)
-    _23 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _24 = torch.einsum("...bd, bdk->...bk", _23)
-    input2 = torch.add(torch.add(_22, _24), torch.select(b1, 0, 1))
-    _25 = torch.relu_(input2)
-    _26 = torch.einsum("...bd, bdk->...bk", [_20, torch.select(w2, 0, 0)])
-    _27 = torch.einsum("...bd, bdk->...bk", [_25, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_26, _27), torch.select(b2, 0, 0))
-    _28 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _29 = torch.einsum("...bd, bdk->...bk", [_25, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_28, _29), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _30 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _31 = int(_30)
-    _32 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_5, _31, int(_32), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_4, _8, _9])
-    return torch.add(x6, bias0)
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_698.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_699.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_700.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_701.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_702.Dropout
-  def forward(self: __torch__.fcu.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _33 = (act).forward((fc1).forward(argument_1, ), )
-    _34 = (fc3).forward((drop).forward(_33, ), )
-    return (drop).forward1(_34, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu.py.debug_pkl
deleted file mode 100644
index a36cd1d9dcdb07068baa420029fb1ac821371be8..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ef26725d9ec7d474a9ad79c6f6ecae7bccd577af36dc2769d163ab5e830a4f43
-size 13848
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_706.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_706.py
deleted file mode 100644
index 24f11bfb5025bebe77c9e77e87c687f285e6e103..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_706.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_704.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_705.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_706.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_706.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_706.py.debug_pkl
deleted file mode 100644
index 5673061841937d91151ba53a7aa565ae65f82c7e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_706.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c1da21c3c722c13be415cb90a2e95325ed6f85ef989c25309f53f91d70201776
-size 13614
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_714.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_714.py
deleted file mode 100644
index 2cd22cbfc39c1b63f28d2a86a80d962c0b657d43..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_714.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_709.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_710.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_711.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_712.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_713.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_714.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_714.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_714.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_714.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_715.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_715.py
deleted file mode 100644
index 8e46abe883d9c4e5bcc080705ae394534ba35056..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_715.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_703.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_706.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_707.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_708.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_714.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_715.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_715.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_715.py.debug_pkl
deleted file mode 100644
index a7ad0a3dd5e9f57f8fbdae533d5296da83d9b1de..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_715.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:58eb0e4335873c00b27643d424827f72262eb5f858bbfac8f7717a964ab1ce09
-size 1496
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_719.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_719.py
deleted file mode 100644
index 3b9fc1085a22df614d1a22642d8177685bab0c0b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_719.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_717.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_718.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_719.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_719.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_719.py.debug_pkl
deleted file mode 100644
index ab83241260703623be6fe8ef54b8bc741425eb49..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_719.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7fbc87ea86a4909ce3e9c9af64d8e8ee3b9023e8411f866d1329e62e0713901d
-size 13614
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_727.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_727.py
deleted file mode 100644
index aa5499a7f950cc4c15758302f29e731d7358ab09..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_727.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_722.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_723.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_724.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_725.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_726.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_727.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_727.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_727.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_727.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_728.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_728.py
deleted file mode 100644
index 142cc83f4021c365891644552c9178b438051778..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_728.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_716.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_719.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_720.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_721.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_727.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_728.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_728.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_728.py.debug_pkl
deleted file mode 100644
index 5bf122acb6e4e15858c4d9083b4010bca74753e2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_728.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f09920aa3574e21082db930e688e4b0d2f613a5d48d9cb917d3f8194d67fad87
-size 1496
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_732.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_732.py
deleted file mode 100644
index 31003d6d7276b43cc7a672c7fce5cfd88bcdb966..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_732.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_730.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_731.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_732.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_732.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_732.py.debug_pkl
deleted file mode 100644
index b6fea99b916f56913d55f661c28f0c8ed416b153..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_732.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bdabcaeb6d4af329ab7657c39f11866bc596de4f77c44a7b67e5bda85d2aeeb9
-size 13614
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_740.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_740.py
deleted file mode 100644
index 930a786a4bc0008ff13ca06df553fd401494f368..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_740.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_735.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_736.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_737.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_738.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_739.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_740.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_740.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_740.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_740.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_741.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_741.py
deleted file mode 100644
index bc5fb45af290e57386e00835a605f429a79c75d1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_741.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_729.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_732.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_733.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_734.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_740.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_741.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_741.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_741.py.debug_pkl
deleted file mode 100644
index 3edfdfeee9be5bb6bb2bea46a26af6651a4519cd..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_741.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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-size 1496
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_745.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_745.py
deleted file mode 100644
index cd9b935344c492e54e654e80afffc77a93114a1c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_745.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_743.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_744.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_745.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_745.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_745.py.debug_pkl
deleted file mode 100644
index 46e60eb34794875e2a3a2f41215f060538c3c506..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_745.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3dfd744a3c2b29ec5fc36d27c55c8c559858d8371359fae9c45a9c997c73669b
-size 13614
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_753.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_753.py
deleted file mode 100644
index bb0c094493892bc72f64fe4fd39ca728beb9edc7..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_753.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_748.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_749.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_750.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_751.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_752.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_753.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_753.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_753.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_753.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_754.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_754.py
deleted file mode 100644
index 7a2d10f6f720b0e6b3a0e9b7ecf6b00863b05f22..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_754.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_742.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_745.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_746.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_747.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_753.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_754.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_754.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_754.py.debug_pkl
deleted file mode 100644
index 7e848852c04182e83697deee100db43a02ea1185..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_754.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ca5cbc0564bb9772bd6f52c19b6071fe9aa35ebb95d553bad01ee0056026ad50
-size 1496
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_758.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_758.py
deleted file mode 100644
index d3d7ddda308f7572872064923a93be63162cf6bc..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_758.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_756.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_757.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_758.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_758.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_758.py.debug_pkl
deleted file mode 100644
index 06da36d0c5b63cae81a37ad367e32ce8f9d381fb..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_758.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bd210f726068fb1ad56b0b14cd05ba9704b853fd278b1f5e16eeaef954226b76
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diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_766.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_766.py
deleted file mode 100644
index a3a6ec04b5aa842b4396cb9675fdb6476e1fb835..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_766.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_761.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_762.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_763.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_764.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_765.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_766.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_766.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_766.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_766.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
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diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_767.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_767.py
deleted file mode 100644
index 2dda3658bf454e45c5ba3f96823f3af409724d72..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_767.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_755.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_758.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_759.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_760.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_766.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_767.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_767.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_767.py.debug_pkl
deleted file mode 100644
index cbd36fef1ba6caef72f948bcf8bc76ac98d94d30..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_767.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_771.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_771.py
deleted file mode 100644
index 525b195fb129279799e074d74e36a2fe022c15b3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_771.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_769.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_770.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_771.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_771.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_771.py.debug_pkl
deleted file mode 100644
index 3b813fb6e6b7bc5498209dd590ef531339bf6eb7..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_771.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3ee0409bb915d8b4f262426358fd8eb9938bf0c12bebcecedd76707a809aca83
-size 13614
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_779.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_779.py
deleted file mode 100644
index 5cbcb039fd1fc5d41334721dfb5a8fdde0bee381..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_779.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_774.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_775.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_776.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_777.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_778.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_779.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_779.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_779.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_779.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_780.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_780.py
deleted file mode 100644
index 3b713c8e45d2c190ca664474ab30ddfe1fabc5f8..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_780.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_768.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_771.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_772.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_773.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_779.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_780.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_780.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_780.py.debug_pkl
deleted file mode 100644
index 70ace20bcc1b9e06a3a9e5ac60b7e8bc4d6a9031..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_780.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:75e96d23e2ba4e27246a8de5cf891dc1b5e2db247a6447b1c747f214ee9404be
-size 1496
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_784.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_784.py
deleted file mode 100644
index 590cdb028d7ac43be1a3ccd854986437fb08d521..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_784.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_782.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_783.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_784.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_784.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_784.py.debug_pkl
deleted file mode 100644
index ad3b05cf459a2d55cb80ac9f2d142e91ad064485..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_784.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4e36a400ae8ac1d8eaa634e0f06e168c6a50b7def79b5d23e47eace33ddc650d
-size 13614
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_792.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_792.py
deleted file mode 100644
index 5fbb5fe82bc52bdf3fb1ead0ff698d44340b8488..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_792.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_787.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_788.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_789.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_790.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_791.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_792.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_792.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_792.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_792.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_793.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_793.py
deleted file mode 100644
index 2382fc7122f1053c5461bc3be1dc74dbf3803e62..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_793.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_781.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_784.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_785.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_786.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_792.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_793.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_793.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_793.py.debug_pkl
deleted file mode 100644
index 0e9bdb6973cf761a4a9ac95fc8c4b74c17431241..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_793.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c04e6d2557c7c539cd0542ef0b7229d6c770458c828a02f17bacef9788a16d85
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diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_797.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_797.py
deleted file mode 100644
index 9dfe5fe9ec9c3a9e42cdaa91704662dcf5030582..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_797.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_795.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_796.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_797.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_797.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_797.py.debug_pkl
deleted file mode 100644
index 2e8fd674d522435123bc86be384c51f780d63773..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_797.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a84d6db47dffb404dccd3e5de08477bb161f2e124aa96d54e6d60e8d78ef4be9
-size 13614
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_805.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_805.py
deleted file mode 100644
index 0ccc6e2c2d390b2eed5c153f60ff638b5e481500..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_805.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_800.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_801.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_802.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_803.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_804.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_805.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_805.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_805.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_805.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_806.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_806.py
deleted file mode 100644
index f2a50da19933288ba8096e0be7423cb19e5cb6cf..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_806.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_794.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_797.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_798.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_799.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_805.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_806.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_806.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_806.py.debug_pkl
deleted file mode 100644
index 012fb7a8d04bb9a79210bf55bdf4f0dd87706e5d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_806.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:203c106cd10504ff21ae619d569021153d7e4a5be12d67d8823c7fac75e6d7bb
-size 1496
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_810.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_810.py
deleted file mode 100644
index 09cfff346f1441730b09f973d3203e979df96920..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_810.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_808.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_809.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_810.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_810.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_810.py.debug_pkl
deleted file mode 100644
index f177fd8bfed485cf91473a41e406ad1bf0f15aa5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_810.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:18342d7318cbba07d6552a57762c12463975e70e278ea6107261cc021b678827
-size 13614
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_818.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_818.py
deleted file mode 100644
index 783accb582d2e23d64375c4c38d40f85942f6e37..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_818.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_813.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_814.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_815.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_816.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_817.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_818.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_818.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_818.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_818.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_819.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_819.py
deleted file mode 100644
index bf429e38b5d5a546a13461cd4db7ba4edacae167..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_819.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_807.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_810.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_811.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_812.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_818.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_819.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_819.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_819.py.debug_pkl
deleted file mode 100644
index 239607eb9212510aa631d89cf77a41e638a5c42b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_819.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f84b446aebb1ebe2f281a9e59172766d94a475a5547c5a68db0c8149969e198f
-size 1496
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_823.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_823.py
deleted file mode 100644
index 82e0803af0d81b8930b52242603fa885377f9e95..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_823.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_821.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_822.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_823.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_823.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_823.py.debug_pkl
deleted file mode 100644
index 5802fef8097379af0a4b3fbd1f73839af89d8f53..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_823.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6e0624537f2e46b6c7939397b03c328dda3a097e809b69eeacca3ea8d1915852
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diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_831.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_831.py
deleted file mode 100644
index 7a1b1c1b350eb8015f771dbc69ec4254a944b8c9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_831.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_826.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_827.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_828.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_829.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_830.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_831.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_831.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_831.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_831.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_832.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_832.py
deleted file mode 100644
index 288eb7ba9f0858c54b09d5762efdc1fef61adb59..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_832.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_820.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_823.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_824.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_825.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_831.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_832.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_832.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_832.py.debug_pkl
deleted file mode 100644
index 2999d26e5a1954f5eeb4b84b4dbce7879075edd4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_832.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d5afc7e36854b4199a82de90e0115a3a340c5132179c80891943905762968c11
-size 1496
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_836.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_836.py
deleted file mode 100644
index c22663dbcb67434b37888e7b9d5fe43aae8e2b19..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_836.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_834.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_835.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_836.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_836.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_836.py.debug_pkl
deleted file mode 100644
index ef83a39399daa4840579d3f97e20fb517ab88da8..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_836.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f2358939e889d6c5b22b8380d5c87ed202d3373331389b1c413f47a5182993be
-size 13614
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_844.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_844.py
deleted file mode 100644
index 92a4bc27387dab67deeeab6fdef3482b9ae8a881..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_844.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_839.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_840.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_841.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_842.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_843.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_844.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_844.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_844.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_844.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_845.py b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_845.py
deleted file mode 100644
index 665888b60941e0ea44e778513e539481a01f40c3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_845.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_833.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_836.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_837.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_838.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_844.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_845.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_845.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_845.py.debug_pkl
deleted file mode 100644
index 50401b9ea828ef3fbe1c2b5577ca75056b32753e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/fcu/___torch_mangle_845.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:55d64974e1bf64dd95f18f210a36fb5fc5d393fc642f24e2c9f2f582a5c985cb
-size 1496
diff --git a/xe_weights14/glonet_p2/code/__torch__/modelp2.py b/xe_weights14/glonet_p2/code/__torch__/modelp2.py
deleted file mode 100644
index c3557c182ebe2d9ad6f80fc864c64a8e9288ff08..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modelp2.py
+++ /dev/null
@@ -1,71 +0,0 @@
-class Glonet(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  jump : __torch__.modules1.residual
-  space : __torch__.sblock.FoTF
-  maps : __torch__.utils.Encoder
-  dynamics : __torch__.dblock.TeDev
-  mapsback : __torch__.utils.Decoder
-  def forward(self: __torch__.modelp2.Glonet,
-    input: Tensor) -> Tensor:
-    mapsback = self.mapsback
-    dynamics = self.dynamics
-    maps = self.maps
-    space = self.space
-    jump = self.jump
-    B = ops.prim.NumToTensor(torch.size(input, 0))
-    _0 = int(B)
-    _1 = int(B)
-    T = ops.prim.NumToTensor(torch.size(input, 1))
-    _2 = int(T)
-    _3 = int(T)
-    C = ops.prim.NumToTensor(torch.size(input, 2))
-    _4 = int(C)
-    _5 = int(C)
-    H = ops.prim.NumToTensor(torch.size(input, 3))
-    _6 = int(H)
-    _7 = int(H)
-    W = ops.prim.NumToTensor(torch.size(input, 4))
-    _8 = int(W)
-    _9 = int(W)
-    x_raw = torch.to(input, dtype=6, layout=0, device=torch.device("cuda:0"))
-    skip_feature = torch.contiguous((jump).forward(x_raw, ))
-    x_raw0 = torch.to(x_raw, dtype=6, layout=0, device=torch.device("cuda:0"))
-    spatial_feature = torch.contiguous((space).forward(x_raw0, ))
-    skip_feature0 = torch.detach(skip_feature)
-    spatial_feature0 = torch.detach(spatial_feature)
-    _10 = torch.reshape(spatial_feature0, [-1, _5, _7, _9])
-    spatial_feature1 = torch.contiguous(_10)
-    input0 = torch.to(spatial_feature1, dtype=6, layout=0, device=torch.device("cuda:0"))
-    _11, _12, = (maps).forward(input0, )
-    spatial_embed = torch.detach(_11)
-    spatial_skip_feature = torch.detach(_12)
-    spatial_embed0 = torch.contiguous(spatial_embed)
-    spatial_skip_feature0 = torch.contiguous(spatial_skip_feature)
-    C_ = ops.prim.NumToTensor(torch.size(spatial_embed0, 1))
-    _13 = int(C_)
-    _14 = int(C_)
-    H_ = ops.prim.NumToTensor(torch.size(spatial_embed0, 2))
-    _15 = int(H_)
-    _16 = int(H_)
-    W_ = ops.prim.NumToTensor(torch.size(spatial_embed0, 3))
-    _17 = int(W_)
-    _18 = torch.view(spatial_embed0, [_1, _3, _14, _16, int(W_)])
-    spatial_embed1 = torch.contiguous(_18)
-    x = torch.to(spatial_embed1, dtype=6, layout=0, device=torch.device("cuda:0"))
-    spatialtemporal_embed = torch.contiguous((dynamics).forward(x, ))
-    spatialtemporal_embed0 = torch.detach(spatialtemporal_embed)
-    _19 = [int(torch.mul(B, T)), _13, _15, _17]
-    _20 = torch.reshape(spatialtemporal_embed0, _19)
-    spatialtemporal_embed1 = torch.contiguous(_20)
-    input1 = torch.to(spatialtemporal_embed1, dtype=6, layout=0, device=torch.device("cuda:0"))
-    enc1 = torch.to(spatial_skip_feature0, dtype=6, layout=0, device=torch.device("cuda:0"))
-    predictions = torch.contiguous((mapsback).forward(input1, enc1, ))
-    predictions0 = torch.detach(predictions)
-    _21 = torch.reshape(predictions0, [_0, _2, _4, _6, _8])
-    _22 = torch.mul(torch.contiguous(_21), CONSTANTS.c0)
-    skip_feature1 = torch.to(skip_feature0, dtype=6, layout=0, device=torch.device("cuda:0"))
-    predictions1 = torch.add(_22, skip_feature1)
-    return torch.contiguous(predictions1)
diff --git a/xe_weights14/glonet_p2/code/__torch__/modelp2.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modelp2.py.debug_pkl
deleted file mode 100644
index db06220face55f736d6b9c5fde1fc2fd0db348d5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modelp2.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2fbc77477363bedad9979f505cb4cc68b9f247f03584afc3d67986892705fe28
-size 7254
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1.py b/xe_weights14/glonet_p2/code/__torch__/modules1.py
deleted file mode 100644
index 0253a936e5b3db7c4dd2ac068e45e0bcb1ce6164..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1.py
+++ /dev/null
@@ -1,222 +0,0 @@
-class residual(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  maps : __torch__.modules1.Encoder
-  mmb : __torch__.modules1.MMB
-  mapsback : __torch__.modules1.Decoder
-  def forward(self: __torch__.modules1.residual,
-    x_raw: Tensor) -> Tensor:
-    mapsback = self.mapsback
-    mmb = self.mmb
-    maps = self.maps
-    B = ops.prim.NumToTensor(torch.size(x_raw, 0))
-    _0 = int(B)
-    _1 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x_raw, 1))
-    _2 = int(T)
-    _3 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x_raw, 2))
-    _4 = int(C)
-    _5 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x_raw, 3))
-    _6 = int(H)
-    _7 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x_raw, 4))
-    _8 = int(W)
-    _9 = int(W)
-    input = torch.view(x_raw, [int(torch.mul(B, T)), _5, _7, _9])
-    _10, _11, = (maps).forward(input, )
-    C_ = ops.prim.NumToTensor(torch.size(_10, 1))
-    _12 = int(C_)
-    _13 = int(C_)
-    H_ = ops.prim.NumToTensor(torch.size(_10, 2))
-    _14 = int(H_)
-    _15 = int(H_)
-    W_ = ops.prim.NumToTensor(torch.size(_10, 3))
-    _16 = int(W_)
-    x = torch.view(_10, [_1, _3, _13, _15, int(W_)])
-    _17 = (mmb).forward(x, )
-    _18 = [int(torch.mul(B, T)), _12, _14, _16]
-    input0 = torch.reshape(_17, _18)
-    _19 = torch.reshape((mapsback).forward(input0, _11, ), [_0, _2, _4, _6, _8])
-    return _19
-class Encoder(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  enc : __torch__.torch.nn.modules.container.Sequential
-  def forward(self: __torch__.modules1.Encoder,
-    input: Tensor) -> Tuple[Tensor, Tensor]:
-    enc = self.enc
-    _3 = getattr(enc, "3")
-    enc0 = self.enc
-    _2 = getattr(enc0, "2")
-    enc1 = self.enc
-    _1 = getattr(enc1, "1")
-    enc2 = self.enc
-    _0 = getattr(enc2, "0")
-    _20 = (_0).forward(input, )
-    _21 = (_3).forward((_2).forward((_1).forward(_20, ), ), )
-    return (_21, _20)
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.BasicConv2d
-  def forward(self: __torch__.modules1.ConvSC,
-    input: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(input, )
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.GroupNorm
-  act : __torch__.torch.nn.modules.activation.LeakyReLU
-  def forward(self: __torch__.modules1.BasicConv2d,
-    input: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _22 = (norm).forward((conv).forward(input, ), )
-    return (act).forward(_22, )
-class MMB(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  enc : __torch__.torch.nn.modules.container.___torch_mangle_165.Sequential
-  dec : __torch__.torch.nn.modules.container.___torch_mangle_318.Sequential
-  def forward(self: __torch__.modules1.MMB,
-    x: Tensor) -> Tensor:
-    dec = self.dec
-    _7 = getattr(dec, "7")
-    dec0 = self.dec
-    _6 = getattr(dec0, "6")
-    dec1 = self.dec
-    _5 = getattr(dec1, "5")
-    dec2 = self.dec
-    _4 = getattr(dec2, "4")
-    dec3 = self.dec
-    _3 = getattr(dec3, "3")
-    dec4 = self.dec
-    _2 = getattr(dec4, "2")
-    dec5 = self.dec
-    _1 = getattr(dec5, "1")
-    dec6 = self.dec
-    _0 = getattr(dec6, "0")
-    enc = self.enc
-    _70 = getattr(enc, "7")
-    enc3 = self.enc
-    _60 = getattr(enc3, "6")
-    enc4 = self.enc
-    _50 = getattr(enc4, "5")
-    enc5 = self.enc
-    _40 = getattr(enc5, "4")
-    enc6 = self.enc
-    _30 = getattr(enc6, "3")
-    enc7 = self.enc
-    _20 = getattr(enc7, "2")
-    enc8 = self.enc
-    _10 = getattr(enc8, "1")
-    enc9 = self.enc
-    _00 = getattr(enc9, "0")
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _23 = int(B)
-    _24 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _25 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _26 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _27 = int(H)
-    _28 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _29 = int(W)
-    _8 = int(W)
-    _31 = [_24, int(torch.mul(T, C)), _28, _8]
-    input = torch.reshape(x, _31)
-    _32 = (_00).forward(input, )
-    _33 = (_10).forward(_32, )
-    _34 = (_20).forward(_33, )
-    _35 = (_30).forward(_34, )
-    _36 = (_40).forward(_35, )
-    _37 = (_50).forward(_36, )
-    _38 = (_60).forward(_37, )
-    _39 = (_0).forward((_70).forward(_38, ), )
-    input1 = torch.cat([_39, _38], 1)
-    input2 = torch.cat([(_1).forward(input1, ), _37], 1)
-    input3 = torch.cat([(_2).forward(input2, ), _36], 1)
-    input4 = torch.cat([(_3).forward(input3, ), _35], 1)
-    input5 = torch.cat([(_4).forward(input4, ), _34], 1)
-    input6 = torch.cat([(_5).forward(input5, ), _33], 1)
-    input7 = torch.cat([(_6).forward(input6, ), _32], 1)
-    hid = torch.reshape((_7).forward(input7, ), [_23, _25, _26, _27, _29])
-    return hid
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_15.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_31.Sequential
-  def forward(self: __torch__.modules1.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _40 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_40, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_40, ))
-    y1 = torch.add_(y0, (_2).forward(_40, ))
-    return torch.add_(y1, (_3).forward(_40, ))
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_16.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_17.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_18.LeakyReLU
-  def forward(self: __torch__.modules1.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _41 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_41, )
-class Decoder(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  dec : __torch__.torch.nn.modules.container.___torch_mangle_338.Sequential
-  readout : __torch__.torch.nn.modules.conv.___torch_mangle_339.Conv2d
-  def forward(self: __torch__.modules1.Decoder,
-    input: Tensor,
-    argument_2: Tensor) -> Tensor:
-    readout = self.readout
-    dec = self.dec
-    _3 = getattr(dec, "3")
-    dec7 = self.dec
-    _2 = getattr(dec7, "2")
-    dec8 = self.dec
-    _1 = getattr(dec8, "1")
-    dec9 = self.dec
-    _0 = getattr(dec9, "0")
-    _42 = (_1).forward((_0).forward(input, ), )
-    input8 = torch.cat([(_2).forward(_42, ), argument_2], 1)
-    _43 = (readout).forward((_3).forward(input8, ), )
-    return _43
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1.py.debug_pkl
deleted file mode 100644
index c1ecf8fdc5688241e04b0ba08a320fdf8c27a546..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:33a52756d283953714a05afaf61d749a4ace3b80c2f02ac86752a63985db81dd
-size 7538
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_101.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_101.py
deleted file mode 100644
index f9a74041bab483dfe71b512f03d62ed0fcf75386..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_101.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_98.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_99.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_100.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_101.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_101.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_101.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_101.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_105.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_105.py
deleted file mode 100644
index 3601e011dc40daebd9d83fa3f501862a1531b445..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_105.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_102.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_103.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_104.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_105.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_105.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_105.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_105.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_107.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_107.py
deleted file mode 100644
index ec9589fe70863f9810dbfc9174a8df832c17d8a0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_107.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_89.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_106.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_107.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_107.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_107.py.debug_pkl
deleted file mode 100644
index 78539907730b8f9f52bdc38157c11485916c2e7e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_107.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_112.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_112.py
deleted file mode 100644
index 045c34f5930bcadffff47039b92e1fd07f3abf54..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_112.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_109.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_110.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_111.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_112.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_112.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_112.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_112.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_116.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_116.py
deleted file mode 100644
index a4410c0312526fd159891f3879feb436a36af77c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_116.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_113.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_114.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_115.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_116.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_116.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_116.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_116.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_120.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_120.py
deleted file mode 100644
index 14e59d5ea63a5ec923d146d22a44fad132a83cec..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_120.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_117.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_118.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_119.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_120.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_120.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_120.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_120.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_124.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_124.py
deleted file mode 100644
index 8abb6802f44aa021056e2f53e90ea08b30c363f0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_124.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_121.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_122.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_123.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_124.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_124.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_124.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_124.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_126.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_126.py
deleted file mode 100644
index c8c40d24db8db81b213bf0b3785ed305aa681273..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_126.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_108.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_125.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_126.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_126.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_126.py.debug_pkl
deleted file mode 100644
index 329bae7d7206ba903598605cc1714f969f61e532..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_126.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1538d6527a843dc8545a28b196f9d2794ddbf7da234e4c619551d1bbf6137ab3
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_13.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_13.py
deleted file mode 100644
index 064575a7f51c8add32bb5447ac447876a1cb378e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_13.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_10.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_11.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_12.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_13.BasicConv2d,
-    argument_1: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_13.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_13.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_13.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_131.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_131.py
deleted file mode 100644
index 518b47eff50c07ab7f644e68c95d92320e9c992b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_131.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_128.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_129.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_130.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_131.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_131.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_131.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_131.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_135.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_135.py
deleted file mode 100644
index 1e49a731aac1fe53cfe3fb31b3c49d12feb604d9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_135.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_132.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_133.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_134.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_135.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_135.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_135.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_135.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_139.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_139.py
deleted file mode 100644
index 2b0089046aa10beb64dabea83d3692f57cd7381e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_139.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_136.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_137.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_138.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_139.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_139.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_139.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_139.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_14.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_14.py
deleted file mode 100644
index 58e0e7b8ca680701c9bfc7a826397fd73f6c195a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_14.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_13.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_14.ConvSC,
-    argument_1: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(argument_1, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_14.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_14.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_14.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_143.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_143.py
deleted file mode 100644
index 698d939739721f3790ea914e70de543e2bf04a98..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_143.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_140.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_141.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_142.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_143.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_143.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_143.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_143.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_145.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_145.py
deleted file mode 100644
index 0a37ecead870dc33645db34e215c419eae64fd68..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_145.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_127.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_144.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_145.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_145.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_145.py.debug_pkl
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index 59755e1e83e953e73e6514e68aa8095544f57ff1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_145.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_150.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_150.py
deleted file mode 100644
index dd8aa7cf1be9261e6a87caa797c3ed3f70171bbe..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_150.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_147.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_148.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_149.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_150.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_150.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_150.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_150.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_154.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_154.py
deleted file mode 100644
index 272b4264d3bd3dea37b08581b6e999fd38ee09f2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_154.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_151.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_152.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_153.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_154.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_154.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_154.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_154.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_158.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_158.py
deleted file mode 100644
index 4dab7d854d992394ada0784366de5b520c3ebc9b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_158.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_155.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_156.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_157.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_158.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_158.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_158.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_158.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_162.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_162.py
deleted file mode 100644
index 3a0d823f1576df444a778d6935cf1eed5d088eed..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_162.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_159.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_160.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_161.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_162.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_162.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_162.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_162.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_164.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_164.py
deleted file mode 100644
index 6ba1d16ff09ccabd1114829e516bed268a6dedec..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_164.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_146.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_163.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_164.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_164.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_164.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_164.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_170.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_170.py
deleted file mode 100644
index d56d33dd83c49e39d1c8eceb3663325cde990fcb..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_170.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_167.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_168.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_169.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_170.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_170.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_170.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_170.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_174.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_174.py
deleted file mode 100644
index 5aa4b48ef95dbd0058b73b6d28de792aa3dbfb9a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_174.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_171.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_172.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_173.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_174.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_174.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_174.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_174.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_178.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_178.py
deleted file mode 100644
index 1dfa1eae8acfc0a33e41b729e4478d75d31dc07c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_178.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_175.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_176.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_177.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_178.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_178.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_178.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_178.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_182.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_182.py
deleted file mode 100644
index 3ca80905b52b486b4b51513488830889ba33004c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_182.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_179.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_180.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_181.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_182.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_182.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_182.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_182.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_184.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_184.py
deleted file mode 100644
index c79abd3ffeb8159a22f92954e0f6736372ae1d62..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_184.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_166.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_183.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_184.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_184.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_184.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_184.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_189.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_189.py
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index 374918f83e34e3f1f2669fe7f1d6b42c4cf42673..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_189.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_186.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_187.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_188.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_189.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_189.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_189.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_189.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_193.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_193.py
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index 27f50ba5eac3b4c3aafd7414120ee20819c243e0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_193.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_190.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_191.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_192.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_193.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_193.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_193.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_193.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_197.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_197.py
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index 8b5d782cd0a887423ecb3212861e106bf4b1a8e4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_197.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_194.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_195.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_196.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_197.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_197.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_197.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_197.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_201.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_201.py
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index 0fe8376b95a9b76d1ed68593ede48e39d9bbe3d4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_201.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_198.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_199.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_200.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_201.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_201.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_201.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_201.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_203.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_203.py
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index 97ba5f416b3bc47cdda06fa2c91d3f15c5a75ea4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_203.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_185.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_202.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_203.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_203.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_203.py.debug_pkl
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index f645ffc58ef35fab9a29e43f342501fb08f8ec54..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_203.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_208.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_208.py
deleted file mode 100644
index 5f3576d4f0499629cb1887c385fea5ce1eec495f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_208.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_205.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_206.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_207.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_208.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_208.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_208.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_208.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_212.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_212.py
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_212.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_209.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_210.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_211.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_212.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_212.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_212.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_212.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_216.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_216.py
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index 288cbc5c962c6d8984367ff15eb2d68468d6520b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_216.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_213.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_214.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_215.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_216.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_216.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_216.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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index ccd37a44494ff0ee6e524541b428ceb54be1c1d9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_22.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_19.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_20.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_21.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_22.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_22.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_22.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_22.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_220.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_220.py
deleted file mode 100644
index d86a5e3c8578b29e9a03886f02a33d8767be1701..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_220.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_217.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_218.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_219.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_220.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_220.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_220.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_220.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_222.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_222.py
deleted file mode 100644
index c471b1e09c8557dfaf689f5ab996f4d0cf5fba33..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_222.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_204.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_221.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_222.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_222.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_222.py.debug_pkl
deleted file mode 100644
index bfa6009ccf54df573352fe3bdefabfd07dc0200e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_222.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_227.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_227.py
deleted file mode 100644
index 473f29ff30984002139b3dd6571cdbeadf9bbdfe..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_227.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_224.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_225.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_226.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_227.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_227.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_227.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_227.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_231.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_231.py
deleted file mode 100644
index 8bcf3690c09edb8e909b0812fedf35cef328d348..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_231.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_228.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_229.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_230.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_231.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_231.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_231.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_231.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_235.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_235.py
deleted file mode 100644
index 3e75d8f05cc010ac1960c0ece10f9840bb061a28..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_235.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_232.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_233.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_234.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_235.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_235.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_235.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_235.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_239.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_239.py
deleted file mode 100644
index cca638271d6ff60b320a3e7e5f1546a23ffa29e3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_239.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_236.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_237.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_238.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_239.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_239.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_239.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_239.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_241.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_241.py
deleted file mode 100644
index 842854ff37d8c71a0bbf8040323c3197e2eaf9fc..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_241.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_223.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_240.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_241.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_241.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_241.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_241.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_246.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_246.py
deleted file mode 100644
index e7fd8cb4a952e44b9320abcb8d9fec145872ebcd..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_246.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_243.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_244.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_245.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_246.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_246.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_246.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_246.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_250.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_250.py
deleted file mode 100644
index 1156b53d651b031c2fe92ae82984ceba9b525396..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_250.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_247.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_248.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_249.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_250.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_250.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_250.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_250.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_254.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_254.py
deleted file mode 100644
index 2cb2fecb3218be19bf1f641066d76b904641c5a5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_254.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_251.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_252.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_253.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_254.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_254.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_254.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_258.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_258.py
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index ac175524a5759a7a70f595be15ce1ada11b8189e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_258.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_255.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_256.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_257.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_258.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_258.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_258.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_26.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_26.py
deleted file mode 100644
index 3abdac87068ff67ba13a4d262f53bd92cfd73de4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_26.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_23.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_24.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_25.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_26.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_26.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_26.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_26.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_260.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_260.py
deleted file mode 100644
index 21115b74a3cafbe83614b2a3b08147edf3ae1aa2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_260.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_242.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_259.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_260.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_260.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_260.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_260.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_265.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_265.py
deleted file mode 100644
index 991b334cb1f77cbaf128df235f41c001d91c2fed..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_265.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_262.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_263.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_264.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_265.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_265.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_265.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_265.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
index 335c4627bc67f03bf85cd459b5aa9fa620723f52..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_269.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_266.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_267.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_268.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_269.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_269.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_269.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_269.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_273.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_270.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_271.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_272.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_273.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_273.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_273.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_273.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_277.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_277.py
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_277.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_274.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_275.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_276.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_277.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_277.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_277.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_277.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_279.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_279.py
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index b3ec377474b06574f47aac871804128acceec749..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_279.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_261.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_278.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_279.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_279.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_279.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_279.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_284.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_284.py
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_284.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_281.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_282.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_283.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_284.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_284.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_284.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_284.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_288.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_288.py
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index 5581a37dffb35d99a20d8e6d84a88abae32b1bb2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_288.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_285.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_286.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_287.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_288.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_288.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_288.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_288.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_292.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_289.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_290.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_291.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_292.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_292.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_292.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_296.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_293.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_294.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_295.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_296.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_296.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_296.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_298.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
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-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_280.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_297.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_298.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
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-    lls1 = self.lls
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-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_298.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_298.py.debug_pkl
deleted file mode 100644
index a4372016a8415c3255c5c65b418d05f0d7fb5596..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_298.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:167bd842ee230511d7b46a28feee51e0ae77cb8f29a66ebbd56aef85b0822feb
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_3.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_3.py
deleted file mode 100644
index 6bc3334e0ca9d63087543b1bc9430ff113c06abe..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_3.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_0.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_1.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_2.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_3.BasicConv2d,
-    argument_1: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_3.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_3.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_3.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_30.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_30.py
deleted file mode 100644
index f8d979f7756c3d5ceb5bf835ef82c774a49a3d10..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_30.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_27.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_28.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_29.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_30.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_30.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_30.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_30.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_303.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_303.py
deleted file mode 100644
index 297c75f8104ed34caf4fa7cb3e3195d3400ba529..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_303.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_300.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_301.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_302.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_303.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_303.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_303.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_303.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_307.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_307.py
deleted file mode 100644
index 489246f0c8c5f52b03f76015ebcf34c79b2087e5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_307.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_304.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_305.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_306.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_307.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_307.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_307.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_307.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_311.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_311.py
deleted file mode 100644
index ee59d9ee6a7707212b39af335c7a31c51e5072c8..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_311.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_308.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_309.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_310.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_311.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_311.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_311.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_311.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_315.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_315.py
deleted file mode 100644
index 5f6508b1402985003103c183420b1cb8c43d6d86..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_315.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_312.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_313.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_314.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_315.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_315.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_315.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_315.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_317.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_317.py
deleted file mode 100644
index 3e924d467ac042f8518a8c0fc0c762123946a953..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_317.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_299.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_316.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_317.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_317.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_317.py.debug_pkl
deleted file mode 100644
index 509262ccd10421577ac8dbf78dc837e9b56a0386..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_317.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_321.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_321.py
deleted file mode 100644
index 14f6ee47df8fe7509d7518ee9c60872fcae33e55..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_321.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.ConvTranspose2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_319.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_320.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_321.BasicConv2d,
-    input: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(input, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_321.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_321.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_321.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_322.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_322.py
deleted file mode 100644
index 2287c6e69e49765c24b8fab9e2ecdb32edbc04c3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_322.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_321.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_322.ConvSC,
-    input: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(input, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_322.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_322.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_322.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_326.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_326.py
deleted file mode 100644
index 7360d1f5e5c7ce86597d8e9fd84e6087fc4d63e5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_326.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_323.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_324.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_325.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_326.BasicConv2d,
-    argument_1: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_326.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_326.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_326.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_327.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_327.py
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index 8e457b1783c2c4d11979a4fedb23e28b16a88e21..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_327.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_326.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_327.ConvSC,
-    argument_1: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(argument_1, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_327.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_327.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_327.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_331.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_331.py
deleted file mode 100644
index 7fb5bd11ca79ede58dcf502088dc1055ea9bf3b0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_331.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_328.ConvTranspose2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_329.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_330.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_331.BasicConv2d,
-    argument_1: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_331.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_331.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_331.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_332.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_332.py
deleted file mode 100644
index bc366373b54165ad6d9045a5b0f82d1de2b8e462..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_332.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_331.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_332.ConvSC,
-    argument_1: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(argument_1, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_332.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_332.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_332.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_336.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_336.py
deleted file mode 100644
index 200b3a69fe74b4fe41a6d14591334a91b8235b98..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_336.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_333.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_334.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_335.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_336.BasicConv2d,
-    input: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(input, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_336.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_336.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_336.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_337.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_337.py
deleted file mode 100644
index bce89e18ade0fc09cf1b562e8cbf74ffdb944ee7..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_337.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_336.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_337.ConvSC,
-    input: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(input, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_337.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_337.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_337.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_36.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_36.py
deleted file mode 100644
index 10c34c7a7f6369fd3f99c6d327662472643bcfb0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_36.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_33.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_34.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_35.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_36.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_36.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_36.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_36.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_4.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_4.py
deleted file mode 100644
index 9fd0b4d1504046d65339d8e18be9189b99080304..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_4.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_3.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_4.ConvSC,
-    argument_1: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(argument_1, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_4.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_4.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_4.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_40.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_40.py
deleted file mode 100644
index a8aac2c1d7bc5b63f17f7a12355159994c7f5e22..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_40.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_37.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_38.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_39.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_40.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_40.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_40.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_40.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_44.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_44.py
deleted file mode 100644
index fe9773f8c93e2451d9d3cebc303cddb47b6b6ea2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_44.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_41.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_42.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_43.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_44.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_44.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_44.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_44.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_48.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_48.py
deleted file mode 100644
index 9dfbbdc31ccbcfde75395692c2ad026a0ba2fb7d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_48.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_45.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_46.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_47.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_48.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_48.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_48.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_48.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_50.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_50.py
deleted file mode 100644
index ba7b1ccbc0b2b8fff115bd3bc509a872da93752d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_50.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_32.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_49.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_50.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_50.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_50.py.debug_pkl
deleted file mode 100644
index 2bb2aadd45d3f31ee60529a25054115bf841f4e2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_50.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_55.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_55.py
deleted file mode 100644
index 512fbfd10bf28e8cc95c06ee5a9f72d863048ea9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_55.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_52.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_53.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_54.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_55.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_55.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_55.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_55.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_59.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_59.py
deleted file mode 100644
index e9b399310d1dbc67074ba45823c358459c506c9d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_59.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_56.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_57.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_58.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_59.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_59.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_59.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_59.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_63.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_63.py
deleted file mode 100644
index 675d8585333b4647332e1f5af630729b0dd9419b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_63.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_60.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_61.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_62.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_63.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_63.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_63.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_63.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_67.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_67.py
deleted file mode 100644
index 32c6ff38bc16d606aeb7c8b31f83871d4723f4ec..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_67.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_64.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_65.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_66.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_67.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_67.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_67.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_67.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_69.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_69.py
deleted file mode 100644
index cd0727b173cf797925450a629d4a1b3a89d83183..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_69.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_51.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_68.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_69.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_69.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_69.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_69.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_74.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_74.py
deleted file mode 100644
index 00276b44c82a8c1c2573e2845d189ee2c523ee90..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_74.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_71.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_72.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_73.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_74.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_74.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_74.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_74.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_78.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_78.py
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index c384b4f149c93286bacae968a04e5f35b905baec..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_78.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_75.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_76.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_77.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_78.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_78.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_78.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_78.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_8.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_8.py
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index 00185510f0e8482046c4576bbf4c791728b1a8b9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_8.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_5.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_6.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_7.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_8.BasicConv2d,
-    argument_1: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_8.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_8.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_8.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_82.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_82.py
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index 976456dc0867d1a4d07bb9df72f712d7b7f6d21d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_82.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_79.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_80.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_81.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_82.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_82.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_82.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_82.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_86.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_86.py
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index e7c230a56d3f87a5f32cf616fb8e46ff1b3400b4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_86.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_83.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_84.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_85.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_86.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_86.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_86.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_86.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_88.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_88.py
deleted file mode 100644
index b1ae7973553d0e01af3b1461a923850bab494924..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_88.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_70.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_87.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_88.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_88.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_88.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_88.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_9.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_9.py
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index 5a53ffe2f99d9028ca2a22f47e152cde5ca5a71f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_9.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_8.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_9.ConvSC,
-    argument_1: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(argument_1, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_9.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_9.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_9.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_93.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_93.py
deleted file mode 100644
index 1c3d286d216a35b6cd22e3058e70c93177b5fab5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_93.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_90.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_91.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_92.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_93.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_93.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_93.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_93.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_97.py b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_97.py
deleted file mode 100644
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--- a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_97.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_94.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_95.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_96.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_97.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_97.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/modules1/___torch_mangle_97.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/sblock.py b/xe_weights14/glonet_p2/code/__torch__/sblock.py
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index d652501e497dc7f2ff9fb22c85ea6d27f2ac5ec1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/sblock.py
+++ /dev/null
@@ -1,174 +0,0 @@
-class FoTF(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  NN : __torch__.NN.Local_CNN_Branch
-  NO : __torch__.NO.GF_Block
-  up : __torch__.torch.nn.modules.conv.___torch_mangle_529.ConvTranspose2d
-  down : __torch__.torch.nn.modules.conv.___torch_mangle_530.Conv2d
-  conv1x1 : __torch__.torch.nn.modules.conv.___torch_mangle_531.Conv2d
-  def forward(self: __torch__.sblock.FoTF,
-    x_raw: Tensor) -> Tensor:
-    down = self.down
-    conv1x1 = self.conv1x1
-    up = self.up
-    NN = self.NN
-    NO = self.NO
-    B = ops.prim.NumToTensor(torch.size(x_raw, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    _4 = int(B)
-    _5 = int(B)
-    _6 = int(B)
-    _7 = int(B)
-    _8 = int(B)
-    _9 = int(B)
-    _10 = int(B)
-    _11 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x_raw, 1))
-    _12 = int(T)
-    _13 = int(T)
-    _14 = int(T)
-    _15 = int(T)
-    _16 = int(T)
-    _17 = int(T)
-    _18 = int(T)
-    _19 = int(T)
-    _20 = int(T)
-    _21 = int(T)
-    _22 = int(T)
-    _23 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x_raw, 2))
-    _24 = int(C)
-    _25 = int(C)
-    _26 = int(C)
-    _27 = int(C)
-    _28 = int(C)
-    _29 = int(C)
-    _30 = int(C)
-    _31 = int(C)
-    _32 = int(C)
-    _33 = int(C)
-    _34 = int(C)
-    _35 = int(C)
-    _36 = int(C)
-    _37 = int(C)
-    _38 = int(C)
-    _39 = int(C)
-    _40 = int(C)
-    _41 = int(C)
-    _42 = int(C)
-    _43 = int(C)
-    _44 = int(C)
-    _45 = int(C)
-    _46 = int(C)
-    _47 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x_raw, 3))
-    _48 = int(H)
-    _49 = int(H)
-    _50 = int(H)
-    _51 = int(H)
-    _52 = int(H)
-    _53 = int(H)
-    _54 = int(H)
-    _55 = int(H)
-    _56 = int(H)
-    _57 = int(H)
-    _58 = int(H)
-    _59 = int(H)
-    _60 = int(H)
-    _61 = int(H)
-    _62 = int(H)
-    _63 = int(H)
-    _64 = int(H)
-    _65 = int(H)
-    _66 = int(H)
-    _67 = int(H)
-    _68 = int(H)
-    _69 = int(H)
-    _70 = int(H)
-    _71 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x_raw, 4))
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-    _73 = int(W)
-    _74 = int(W)
-    _75 = int(W)
-    _76 = int(W)
-    _77 = int(W)
-    _78 = int(W)
-    _79 = int(W)
-    _80 = int(W)
-    _81 = int(W)
-    _82 = int(W)
-    _83 = int(W)
-    _84 = int(W)
-    _85 = int(W)
-    _86 = int(W)
-    _87 = int(W)
-    _88 = int(W)
-    _89 = int(W)
-    _90 = int(W)
-    _91 = int(W)
-    _92 = int(W)
-    _93 = int(W)
-    _94 = int(W)
-    _95 = int(W)
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-    _97 = (NN).forward(x_raw, )
-    _98 = [int(torch.mul(B, T)), _47, _71, _95]
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-    _99 = [int(torch.mul(B, T)), _45, _69, _93]
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-    _101 = (NN).forward1(x, )
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-    x0 = torch.add(gf_features_down, lc_features0)
-    _104 = (NO).forward2(x0, )
-    _105 = (NN).forward2(x0, )
-    _106 = [int(torch.mul(B, T)), _39, _63, _87]
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-    _107 = [int(torch.mul(B, T)), _37, _61, _85]
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-    lc_features1 = torch.view((conv1x1).forward2(input4, ), [_6, _18, _36, _60, _84])
-    x1 = torch.add(gf_features_up0, lc_features1)
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-    _109 = (NN).forward3(x1, )
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-    _111 = [int(torch.mul(B, T)), _33, _57, _81]
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-    x2 = torch.add(gf_features_down0, lc_features2)
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-    _113 = (NN).forward4(x2, )
-    _114 = [int(torch.mul(B, T)), _31, _55, _79]
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-    gf_features_up1 = torch.view((up).forward2(input7, ), [_3, _15, _30, _54, _78])
-    _115 = [int(torch.mul(B, T)), _29, _53, _77]
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-    lc_features3 = torch.view((conv1x1).forward4(input8, ), [_2, _14, _28, _52, _76])
-    x3 = torch.add(gf_features_up1, lc_features3)
-    _116 = (NO).forward5(x3, )
-    _117 = (NN).forward5(x3, )
-    _118 = [int(torch.mul(B, T)), _27, _51, _75]
-    input9 = torch.view(_116, _118)
-    gf_features_down1 = torch.view((down).forward2(input9, ), [_1, _13, _26, _50, _74])
-    _119 = [int(torch.mul(B, T)), _25, _49, _73]
-    input10 = torch.view(_117, _119)
-    lc_features4 = torch.view((conv1x1).forward5(input10, ), [_0, _12, _24, _48, _72])
-    x4 = torch.add(gf_features_down1, lc_features4)
-    _120 = torch.add((NO).forward6(x4, ), (NN).forward6(x4, ))
-    return _120
diff --git a/xe_weights14/glonet_p2/code/__torch__/sblock.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/sblock.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/sblock.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation.py
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index 80aaecfceef2cc9741ccc2fe1317fa7b0a60de6d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation.py
+++ /dev/null
@@ -1,74 +0,0 @@
-class LeakyReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    input = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return input
-class ReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-class MultiheadAttention(Module):
-  __parameters__ = ["in_proj_weight", "in_proj_bias", ]
-  __buffers__ = []
-  in_proj_weight : Tensor
-  in_proj_bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  out_proj : __torch__.torch.nn.modules.linear.NonDynamicallyQuantizableLinear
-class GELU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward2(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward4(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward6(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
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-  __parameters__ = []
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
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-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
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-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
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-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
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-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward6(self: __torch__.torch.nn.modules.activation.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_100.py
+++ /dev/null
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-class LeakyReLU(Module):
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_100.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_1002.py
+++ /dev/null
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_1002.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    hid = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return hid
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-    argument_1: Tensor) -> Tensor:
-    input = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_1007.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_1007.py.debug_pkl
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_104.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_104.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_104.py.debug_pkl
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-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
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-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_115.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_115.py.debug_pkl
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-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_119.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_119.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_12.py
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-class LeakyReLU(Module):
-  __parameters__ = []
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_12.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    embed = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return embed
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_12.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_12.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_123.py
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-class LeakyReLU(Module):
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-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_123.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_123.py.debug_pkl
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-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_130.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_130.py.debug_pkl
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-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
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-class LeakyReLU(Module):
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_157.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_157.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_161.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_161.py.debug_pkl
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-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_18.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_18.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_181.py
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@@ -1,9 +0,0 @@
-class LeakyReLU(Module):
-  __parameters__ = []
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_181.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_181.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_181.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_188.py
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-class LeakyReLU(Module):
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_21.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_21.py.debug_pkl
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-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
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-class LeakyReLU(Module):
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-class LeakyReLU(Module):
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_291.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_291.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_291.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_291.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_295.py
+++ /dev/null
@@ -1,9 +0,0 @@
-class LeakyReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_295.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_295.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_295.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_302.py
+++ /dev/null
@@ -1,9 +0,0 @@
-class LeakyReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_302.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_302.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_302.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_306.py
+++ /dev/null
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-class LeakyReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_306.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_306.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_306.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_310.py
+++ /dev/null
@@ -1,9 +0,0 @@
-class LeakyReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_310.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_310.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_310.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_314.py
+++ /dev/null
@@ -1,9 +0,0 @@
-class LeakyReLU(Module):
-  __parameters__ = []
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_314.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_314.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_314.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_320.py
+++ /dev/null
@@ -1,9 +0,0 @@
-class LeakyReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_320.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    input = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_320.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_320.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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+++ /dev/null
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-class LeakyReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_325.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    input = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_325.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_325.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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+++ /dev/null
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-class LeakyReLU(Module):
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-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_330.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    hid = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return hid
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_330.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_330.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_330.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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+++ /dev/null
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-class LeakyReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_335.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    input = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_335.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_335.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_335.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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+++ /dev/null
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-class ReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_347.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_347.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_35.py
+++ /dev/null
@@ -1,9 +0,0 @@
-class LeakyReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_35.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_35.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_35.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_35.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_352.py
+++ /dev/null
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-class MultiheadAttention(Module):
-  __parameters__ = ["in_proj_weight", "in_proj_bias", ]
-  __buffers__ = []
-  in_proj_weight : Tensor
-  in_proj_bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  out_proj : __torch__.torch.nn.modules.linear.___torch_mangle_351.NonDynamicallyQuantizableLinear
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_352.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_352.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_356.py
+++ /dev/null
@@ -1,26 +0,0 @@
-class GELU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_356.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.___torch_mangle_356.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward2(self: __torch__.torch.nn.modules.activation.___torch_mangle_356.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_356.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward4(self: __torch__.torch.nn.modules.activation.___torch_mangle_356.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_356.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_356.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_356.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_356.py.debug_pkl
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-class ReLU(Module):
-  __parameters__ = []
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_368.py
+++ /dev/null
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-class MultiheadAttention(Module):
-  __parameters__ = ["in_proj_weight", "in_proj_bias", ]
-  __buffers__ = []
-  in_proj_weight : Tensor
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-  training : bool
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_372.py
+++ /dev/null
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-class GELU(Module):
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_372.GELU,
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-    return torch.gelu(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.___torch_mangle_372.GELU,
-    argument_1: Tensor) -> Tensor:
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-  def forward2(self: __torch__.torch.nn.modules.activation.___torch_mangle_372.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_372.GELU,
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-    return torch.gelu(argument_1)
-  def forward4(self: __torch__.torch.nn.modules.activation.___torch_mangle_372.GELU,
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-    return torch.gelu(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_372.GELU,
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_372.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
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-class ReLU(Module):
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-class MultiheadAttention(Module):
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-class GELU(Module):
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU,
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-    return torch.gelu(argument_1)
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-    return torch.gelu(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU,
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-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU,
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_39.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
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-class ReLU(Module):
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-class MultiheadAttention(Module):
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-  training : bool
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-    return torch.gelu(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.___torch_mangle_404.GELU,
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-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_404.GELU,
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_404.GELU,
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-  def forward1(self: __torch__.torch.nn.modules.activation.___torch_mangle_420.GELU,
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_420.GELU,
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-class ReLU(Module):
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-class LeakyReLU(Module):
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_43.LeakyReLU,
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-class MultiheadAttention(Module):
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_436.GELU,
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_452.GELU,
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-class ReLU(Module):
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_480.py
+++ /dev/null
@@ -1,8 +0,0 @@
-class MultiheadAttention(Module):
-  __parameters__ = ["in_proj_weight", "in_proj_bias", ]
-  __buffers__ = []
-  in_proj_weight : Tensor
-  in_proj_bias : Tensor
-  training : bool
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_484.py
+++ /dev/null
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-class GELU(Module):
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_484.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.___torch_mangle_484.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward2(self: __torch__.torch.nn.modules.activation.___torch_mangle_484.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_484.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward4(self: __torch__.torch.nn.modules.activation.___torch_mangle_484.GELU,
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-    return torch.gelu(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_484.GELU,
-    argument_1: Tensor) -> Tensor:
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_484.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
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-class ReLU(Module):
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_491.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_491.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_496.py
+++ /dev/null
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-class MultiheadAttention(Module):
-  __parameters__ = ["in_proj_weight", "in_proj_bias", ]
-  __buffers__ = []
-  in_proj_weight : Tensor
-  in_proj_bias : Tensor
-  training : bool
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_500.py
+++ /dev/null
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-class GELU(Module):
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU,
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-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU,
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-  def forward4(self: __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU,
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-    return torch.gelu(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU,
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-    return torch.gelu(argument_1)
-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
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-class ReLU(Module):
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-  training : bool
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_507.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/activation/___torch_mangle_507.py.debug_pkl
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-class MultiheadAttention(Module):
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-class GELU(Module):
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-    return torch.gelu(argument_1)
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-    return torch.gelu(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU,
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU,
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-    return torch.gelu(argument_1)
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-class Tanh(Module):
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-    return torch.tanh(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh,
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-    return torch.tanh(argument_1)
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-    return torch.tanh(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh,
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh,
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-    return torch.tanh(argument_1)
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-class LeakyReLU(Module):
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-    spatial_embed = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return spatial_embed
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_723.GELU,
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-class Sequential(Module):
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-class Sequential(Module):
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-class Sequential(Module):
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-class Sequential(Module):
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-class Sequential(Module):
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-class Sequential(Module):
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-class Sequential(Module):
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-class Sequential(Module):
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-class Sequential(Module):
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-  act1 : __torch__.torch.nn.modules.activation.Tanh
-  transposeconv2 : __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d
-  act2 : __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh
-  transposeconv3 : __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d
-  def forward(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
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-    _1 = (act2).forward((transposeconv2).forward(_0, ), )
-    return (transposeconv3).forward(_1, )
-  def forward1(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
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-    _3 = (act2).forward1((transposeconv2).forward1(_2, ), )
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-  def forward2(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
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-    _5 = (act2).forward2((transposeconv2).forward2(_4, ), )
-    return (transposeconv3).forward2(_5, )
-  def forward3(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
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-    _7 = (act2).forward3((transposeconv2).forward3(_6, ), )
-    return (transposeconv3).forward3(_7, )
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-    _9 = (act2).forward4((transposeconv2).forward4(_8, ), )
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-    _11 = (act2).forward5((transposeconv2).forward5(_10, ), )
-    return (transposeconv3).forward5(_11, )
-  def forward6(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
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-    _13 = (act2).forward6((transposeconv2).forward6(_12, ), )
-    return (transposeconv3).forward6(_13, )
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/container/___torch_mangle_527.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/container/___torch_mangle_527.py.debug_pkl
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-class Sequential(Module):
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv.py
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-class Conv2d(Module):
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-  __buffers__ = []
-  weight : Tensor
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-  def forward(self: __torch__.torch.nn.modules.conv.Conv2d,
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-    input0 = torch._convolution(input, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input0
-class ConvTranspose2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.conv.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input1 = torch._convolution(input, weight, bias, [2, 2], [1, 1], [1, 1], True, [1, 1], 1, False, False, True, True)
-    return input1
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-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.Conv1d,
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-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.Conv1d,
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-    return _1
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-    return _2
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-    bias = self.bias
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-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
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-    return _4
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-    return _5
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-    return _6
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-    return input
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-    return input
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index 40de54cbabfa61642f3075939d83dee29030aae5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_1000.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class ConvTranspose2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_1000.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    input0 = torch._convolution(input, weight, bias, [2, 2], [1, 1], [1, 1], True, [1, 1], 1, False, False, True, True)
-    return input0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_1000.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_1000.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_1005.py
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-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
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-  training : bool
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-    input: Tensor) -> Tensor:
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-    input0 = torch._convolution(input, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input0
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-class Conv2d(Module):
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-    argument_1: Tensor) -> Tensor:
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-    return _0
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-    return input
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-    return input
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-    return input
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-    return input
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [3, 3], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_136.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_136.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_140.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_140.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [5, 5], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_140.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_140.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_146.py
+++ /dev/null
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-class Conv2d(Module):
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-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_146.Conv2d,
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_146.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_146.py.debug_pkl
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-class Conv2d(Module):
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-    argument_1: Tensor) -> Tensor:
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_147.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_147.py.debug_pkl
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-class Conv2d(Module):
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-    return input0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_15.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_15.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_151.py
+++ /dev/null
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_151.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_151.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_155.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_155.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_155.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_159.py
+++ /dev/null
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_159.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_159.py.debug_pkl
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+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_16.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_16.py.debug_pkl
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_166.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_166.py.debug_pkl
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_167.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_167.py.debug_pkl
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_171.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_171.py.debug_pkl
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_175.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_175.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_179.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_179.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [5, 5], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_179.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_179.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_185.py
+++ /dev/null
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-class Conv2d(Module):
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-    input: Tensor) -> Tensor:
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-    input0 = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_185.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_185.py.debug_pkl
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-class Conv2d(Module):
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-    return input
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+++ /dev/null
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-class Conv2d(Module):
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-    return input
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-    return input
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-class Conv2d(Module):
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-    return input
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-    return input
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_204.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_204.py.debug_pkl
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-    return input
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-    input0 = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_223.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_223.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_224.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
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-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_224.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_224.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_224.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_228.py
+++ /dev/null
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-class Conv2d(Module):
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-    bias = self.bias
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_228.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_228.py.debug_pkl
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-class Conv2d(Module):
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-    bias = self.bias
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_23.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_23.py.debug_pkl
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_232.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_232.py.debug_pkl
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-class Conv2d(Module):
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-    return input
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+++ /dev/null
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-class Conv2d(Module):
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-    return input0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_242.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_242.py.debug_pkl
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_243.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_243.py.debug_pkl
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_247.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_247.py.debug_pkl
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_251.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_251.py.debug_pkl
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_255.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_255.py.debug_pkl
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-    return input0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_261.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_261.py.debug_pkl
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_262.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_262.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_266.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
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-    argument_1: Tensor) -> Tensor:
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [2, 2], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_266.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_266.py.debug_pkl
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-class Conv2d(Module):
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-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [5, 5], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_27.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_27.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_270.py
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-class Conv2d(Module):
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-    return input
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-    return input
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-class Conv2d(Module):
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-    return input0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_280.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_280.py.debug_pkl
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-    return input
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_285.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_285.py.debug_pkl
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-class Conv2d(Module):
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-    return input
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [3, 3], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_308.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_308.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_312.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_312.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [5, 5], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_312.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_312.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_32.py
+++ /dev/null
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-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_32.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_32.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_32.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_323.py
+++ /dev/null
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-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_323.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_323.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_323.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_328.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class ConvTranspose2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_328.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [2, 2], [1, 1], [1, 1], True, [1, 1], 1, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_328.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_328.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_33.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_33.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_33.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_33.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_333.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-  __buffers__ = []
-  weight : Tensor
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_333.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    input0 = torch._convolution(input, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_333.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_333.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_339.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_339.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    Y = torch._convolution(argument_1, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return Y
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_339.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_339.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_340.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class ConvTranspose2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_340.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_340.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_341.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  training : bool
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-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
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-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
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-    _6 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_341.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_341.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_348.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
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-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_348.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_348.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_364.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_364.py
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index 6471975f59c6c466ba59c55d4d2ca40bacc6a800..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_364.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_364.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_364.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_37.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_37.py
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index c19b0f80d404590ab9f87a88677ba1aa628bf737..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_37.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_37.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [2, 2], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_37.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_37.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_380.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_380.py
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index 6bfcbd12dd469899fd3dd54fb1d8282d1849e53d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_380.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_380.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_380.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_396.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_396.py
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index adb17b3fa270b608ce0b9cd8dbe683faee0d41b2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_396.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_396.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_396.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_41.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_41.py
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_41.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_41.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [3, 3], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_41.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_41.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_412.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_412.py
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index caff6835bf4cbb1932820596d2c0547d4fa7e47c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_412.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_412.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_412.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_428.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_428.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_428.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_428.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_444.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_444.py
deleted file mode 100644
index 8994117ee936358fd2f27a2aa7d5116c9514e783..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_444.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_444.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_444.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_45.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_45.py
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index 2eb40c5bcaffb13985cf24196137b20e945d1f78..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_45.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_45.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [5, 5], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_45.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_45.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_460.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_460.py
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index 50cfa48e281c06ea84bccf71ff751cb9a18cf3c0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_460.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_460.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_460.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_460.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_476.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_476.py
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index 681245c6bd2367174242fb17ee725ece4df69997..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_476.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_476.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_476.py.debug_pkl
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index 3cbac124402a53a5ff424d404104d64df1d32b8d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_492.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_492.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_492.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_5.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_5.py
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_5.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_5.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_5.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_5.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_508.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_508.py
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index 344d38f9c1dab40665d5a5967e59b089e88b3973..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_508.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_508.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_508.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_51.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_51.py
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_51.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_51.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_51.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_51.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_52.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_52.py
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_52.py
+++ /dev/null
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-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_52.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_52.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_52.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_523.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class ConvTranspose2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input0 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input1 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input2 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input3 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input4 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input5 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input6 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_523.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_523.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_524.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class ConvTranspose2d(Module):
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-  __buffers__ = []
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-  training : bool
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_524.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_524.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_526.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class ConvTranspose2d(Module):
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    x = torch._convolution(argument_1, weight, bias, [4, 4], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(argument_1, weight, bias, [4, 4], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(argument_1, weight, bias, [4, 4], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(argument_1, weight, bias, [4, 4], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
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-    return x
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
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-    return x
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return x
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_526.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_526.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_529.py
+++ /dev/null
@@ -1,25 +0,0 @@
-class ConvTranspose2d(Module):
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-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_529.ConvTranspose2d,
-    input: Tensor) -> Tensor:
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-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_529.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return _2
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_530.py
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-class Conv2d(Module):
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-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_530.Conv2d,
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-    return _2
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-class Conv2d(Module):
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-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_531.Conv2d,
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-    bias = self.bias
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-    _2 = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_531.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_531.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_531.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _5
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_531.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_531.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_532.py
+++ /dev/null
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-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
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-    return input0
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-class Conv2d(Module):
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-    return input
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-    return input0
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-    return input
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-    return input
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_568.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_568.py.debug_pkl
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-class Conv2d(Module):
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-    return input
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_616.py
+++ /dev/null
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-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
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-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_616.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_616.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_616.py.debug_pkl
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-class Conv2d(Module):
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-    return input
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-class Conv2d(Module):
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-    return input
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-    return input
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-    return input
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_655.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_655.py.debug_pkl
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-    return input
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_718.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_718.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_718.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_718.py.debug_pkl
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-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
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-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_731.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return _0
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+++ /dev/null
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-class Conv1d(Module):
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-    input: Tensor) -> Tensor:
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-    return _0
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-class Conv2d(Module):
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-    return input
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-    return _0
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-class Conv2d(Module):
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-    return input
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index a6623805878579f2ad2ecc42628882f7cad3eb6d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_89.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_89.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_89.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_89.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_890.py
+++ /dev/null
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-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
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-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_890.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [2, 2], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_890.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_890.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_894.py
+++ /dev/null
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-class Conv2d(Module):
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-    return input
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-class Conv2d(Module):
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-    return input
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_90.py
+++ /dev/null
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-class Conv2d(Module):
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-    return input
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-    return input0
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+++ /dev/null
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-class Conv2d(Module):
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-    return input
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-    return input
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-    return input
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-    return input
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-    return input
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-class Conv2d(Module):
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-    return input
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/conv/___torch_mangle_974.py
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-class Conv2d(Module):
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-    return input
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-    return input
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-    return input
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-    return input
-  def forward13(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _6 = torch.dropout(argument_1, 0., False)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_359.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_359.py.debug_pkl
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index 2785591a9b939cc6856b17171781f5e47624519c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_375.py
+++ /dev/null
@@ -1,61 +0,0 @@
-class Dropout(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
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-    argument_1: Tensor) -> Tensor:
-    _0 = torch.dropout(argument_1, 0., False)
-    return _0
-  def forward2(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _1 = torch.dropout(argument_1, 0., False)
-    return _1
-  def forward4(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
-    argument_1: Tensor) -> Tensor:
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-    return input
-  def forward5(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _2 = torch.dropout(argument_1, 0., False)
-    return _2
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-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
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-    return _3
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-    return input
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-    argument_1: Tensor) -> Tensor:
-    _4 = torch.dropout(argument_1, 0., False)
-    return _4
-  def forward10(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
-    argument_1: Tensor) -> Tensor:
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-    return input
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-    argument_1: Tensor) -> Tensor:
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-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward13(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _6 = torch.dropout(argument_1, 0., False)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_375.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_375.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_391.py
+++ /dev/null
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-class Dropout(Module):
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_391.Dropout,
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-    return input
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-    return _1
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-    return _2
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-    return input
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-    return _3
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-    return input
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-    argument_1: Tensor) -> Tensor:
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-    return _4
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-    return input
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-    input = torch.dropout(argument_1, 0., False)
-    return input
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-    argument_1: Tensor) -> Tensor:
-    _6 = torch.dropout(argument_1, 0., False)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_391.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_391.py.debug_pkl
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+++ /dev/null
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
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-    return input
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-    return _1
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-    return _2
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-    return input
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-    return _3
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-    return input
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-    return _4
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-    return input
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-    argument_1: Tensor) -> Tensor:
-    _6 = torch.dropout(argument_1, 0., False)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_407.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_407.py.debug_pkl
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-    return _3
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-    return _4
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_423.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_423.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_439.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_439.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_455.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_455.py
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_455.py
+++ /dev/null
@@ -1,61 +0,0 @@
-class Dropout(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward1(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.dropout(argument_1, 0., False)
-    return _0
-  def forward2(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _1 = torch.dropout(argument_1, 0., False)
-    return _1
-  def forward4(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _2 = torch.dropout(argument_1, 0., False)
-    return _2
-  def forward6(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward7(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _3 = torch.dropout(argument_1, 0., False)
-    return _3
-  def forward8(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward9(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _4 = torch.dropout(argument_1, 0., False)
-    return _4
-  def forward10(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward11(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _5 = torch.dropout(argument_1, 0., False)
-    return _5
-  def forward12(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward13(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _6 = torch.dropout(argument_1, 0., False)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_455.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_455.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_471.py
+++ /dev/null
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-class Dropout(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_471.Dropout,
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-    input = torch.dropout(argument_1, 0., False)
-    return input
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-    return _0
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-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.dropout.___torch_mangle_471.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _1 = torch.dropout(argument_1, 0., False)
-    return _1
-  def forward4(self: __torch__.torch.nn.modules.dropout.___torch_mangle_471.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.dropout.___torch_mangle_471.Dropout,
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-    _2 = torch.dropout(argument_1, 0., False)
-    return _2
-  def forward6(self: __torch__.torch.nn.modules.dropout.___torch_mangle_471.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward7(self: __torch__.torch.nn.modules.dropout.___torch_mangle_471.Dropout,
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-    return _3
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-    return input
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-    _4 = torch.dropout(argument_1, 0., False)
-    return _4
-  def forward10(self: __torch__.torch.nn.modules.dropout.___torch_mangle_471.Dropout,
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-    return input
-  def forward11(self: __torch__.torch.nn.modules.dropout.___torch_mangle_471.Dropout,
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-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward13(self: __torch__.torch.nn.modules.dropout.___torch_mangle_471.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _6 = torch.dropout(argument_1, 0., False)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_471.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_471.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_487.py
+++ /dev/null
@@ -1,61 +0,0 @@
-class Dropout(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_487.Dropout,
-    argument_1: Tensor) -> Tensor:
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-    return input
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-    return _0
-  def forward2(self: __torch__.torch.nn.modules.dropout.___torch_mangle_487.Dropout,
-    argument_1: Tensor) -> Tensor:
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-    return input
-  def forward3(self: __torch__.torch.nn.modules.dropout.___torch_mangle_487.Dropout,
-    argument_1: Tensor) -> Tensor:
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-    return _1
-  def forward4(self: __torch__.torch.nn.modules.dropout.___torch_mangle_487.Dropout,
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-    return input
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-    _2 = torch.dropout(argument_1, 0., False)
-    return _2
-  def forward6(self: __torch__.torch.nn.modules.dropout.___torch_mangle_487.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward7(self: __torch__.torch.nn.modules.dropout.___torch_mangle_487.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _3 = torch.dropout(argument_1, 0., False)
-    return _3
-  def forward8(self: __torch__.torch.nn.modules.dropout.___torch_mangle_487.Dropout,
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-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward9(self: __torch__.torch.nn.modules.dropout.___torch_mangle_487.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _4 = torch.dropout(argument_1, 0., False)
-    return _4
-  def forward10(self: __torch__.torch.nn.modules.dropout.___torch_mangle_487.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward11(self: __torch__.torch.nn.modules.dropout.___torch_mangle_487.Dropout,
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-    return _5
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-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward13(self: __torch__.torch.nn.modules.dropout.___torch_mangle_487.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _6 = torch.dropout(argument_1, 0., False)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_487.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_487.py.debug_pkl
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+++ /dev/null
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-class Dropout(Module):
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_503.Dropout,
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-    return input
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-    return _0
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-    return input
-  def forward3(self: __torch__.torch.nn.modules.dropout.___torch_mangle_503.Dropout,
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-    return _1
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-    return input
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-    return _2
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-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward7(self: __torch__.torch.nn.modules.dropout.___torch_mangle_503.Dropout,
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-    _3 = torch.dropout(argument_1, 0., False)
-    return _3
-  def forward8(self: __torch__.torch.nn.modules.dropout.___torch_mangle_503.Dropout,
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-    input = torch.dropout(argument_1, 0., False)
-    return input
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-    _4 = torch.dropout(argument_1, 0., False)
-    return _4
-  def forward10(self: __torch__.torch.nn.modules.dropout.___torch_mangle_503.Dropout,
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-    return input
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-    return input
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-    _6 = torch.dropout(argument_1, 0., False)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_503.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_503.py.debug_pkl
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-    return _1
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-    return _2
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-    return input
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-    return input
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-    return _4
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-    return input
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-    _6 = torch.dropout(argument_1, 0., False)
-    return _6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_519.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/dropout/___torch_mangle_519.py.debug_pkl
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-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.linear.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward1(self: __torch__.torch.nn.modules.linear.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward2(self: __torch__.torch.nn.modules.linear.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.linear.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward6(self: __torch__.torch.nn.modules.linear.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear.py.debug_pkl
deleted file mode 100644
index 69348c690af46e88fe1a15500d69fa415df4ddb6..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_342.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_342.py
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_342.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class Identity(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_342.Identity) -> NoneType:
-    return None
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-    return None
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-    return None
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-    return None
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-    return None
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_342.Identity) -> NoneType:
-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_342.Identity) -> NoneType:
-    return None
-  def forward7(self: __torch__.torch.nn.modules.linear.___torch_mangle_342.Identity) -> NoneType:
-    return None
-  def forward8(self: __torch__.torch.nn.modules.linear.___torch_mangle_342.Identity) -> NoneType:
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-    return None
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-    return None
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-    return None
-  def forward13(self: __torch__.torch.nn.modules.linear.___torch_mangle_342.Identity) -> NoneType:
-    return None
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_342.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_342.py.debug_pkl
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+++ /dev/null
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_344.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_350.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_350.py
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_350.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class Identity(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_350.Identity) -> NoneType:
-    return None
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-    return None
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-    return None
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_350.Identity) -> NoneType:
-    return None
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_350.Identity) -> NoneType:
-    return None
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_350.Identity) -> NoneType:
-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_350.Identity) -> NoneType:
-    return None
-  def forward7(self: __torch__.torch.nn.modules.linear.___torch_mangle_350.Identity) -> NoneType:
-    return None
-  def forward8(self: __torch__.torch.nn.modules.linear.___torch_mangle_350.Identity) -> NoneType:
-    return None
-  def forward9(self: __torch__.torch.nn.modules.linear.___torch_mangle_350.Identity) -> NoneType:
-    return None
-  def forward10(self: __torch__.torch.nn.modules.linear.___torch_mangle_350.Identity) -> NoneType:
-    return None
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-    return None
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-  def forward13(self: __torch__.torch.nn.modules.linear.___torch_mangle_350.Identity) -> NoneType:
-    return None
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_350.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_350.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_351.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_351.py
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_351.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class NonDynamicallyQuantizableLinear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_351.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_351.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_355.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
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-    return input
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-    return input
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_355.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_355.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_355.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_355.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_355.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_355.py.debug_pkl
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-class Linear(Module):
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_366.py
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-class Identity(Module):
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-    return None
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-    return None
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-    return None
-  def forward8(self: __torch__.torch.nn.modules.linear.___torch_mangle_366.Identity) -> NoneType:
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-  def forward9(self: __torch__.torch.nn.modules.linear.___torch_mangle_366.Identity) -> NoneType:
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_366.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_366.py.debug_pkl
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-class NonDynamicallyQuantizableLinear(Module):
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-  __buffers__ = []
-  weight : Tensor
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_367.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_367.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_371.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-    return input
-  def forward2(self: __torch__.torch.nn.modules.linear.___torch_mangle_371.Linear,
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-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_371.Linear,
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-    bias = self.bias
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-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_371.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_371.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_371.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_371.py.debug_pkl
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+++ /dev/null
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_373.py
+++ /dev/null
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-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_382.py
+++ /dev/null
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-class Identity(Module):
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-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_382.Identity) -> NoneType:
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-    return None
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-class NonDynamicallyQuantizableLinear(Module):
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_387.py
+++ /dev/null
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-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
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-  weight : Tensor
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-  training : bool
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-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_387.Linear,
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-    return input
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_387.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_387.py.debug_pkl
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-class Linear(Module):
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-class NonDynamicallyQuantizableLinear(Module):
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_403.py
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-class Linear(Module):
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_403.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_403.py.debug_pkl
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-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward7(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
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-    return None
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-    return None
-  def forward13(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_414.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_414.py.debug_pkl
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+++ /dev/null
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_415.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_415.py
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_415.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class NonDynamicallyQuantizableLinear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_415.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_415.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_419.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_419.py
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_419.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward1(self: __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear,
-    argument_1: Tensor) -> Tensor:
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-    return input
-  def forward2(self: __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_419.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_419.py.debug_pkl
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-class Linear(Module):
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_430.py
+++ /dev/null
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-class Identity(Module):
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-    return None
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-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity) -> NoneType:
-    return None
-  def forward7(self: __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity) -> NoneType:
-    return None
-  def forward8(self: __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity) -> NoneType:
-    return None
-  def forward9(self: __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity) -> NoneType:
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_431.py
+++ /dev/null
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-class NonDynamicallyQuantizableLinear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_435.py
+++ /dev/null
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-class Linear(Module):
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-  training : bool
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-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_435.Linear,
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-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_435.Linear,
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-    bias = self.bias
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-    return input
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-    return input
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-    argument_1: Tensor) -> Tensor:
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_435.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_435.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_446.py
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_446.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_446.py.debug_pkl
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-class NonDynamicallyQuantizableLinear(Module):
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_451.py
+++ /dev/null
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-class Linear(Module):
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-  weight : Tensor
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-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_451.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
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-    argument_1: Tensor) -> Tensor:
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-    argument_1: Tensor) -> Tensor:
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-    return input
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_451.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_451.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_451.py.debug_pkl
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-class Linear(Module):
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_462.py
+++ /dev/null
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-class Identity(Module):
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_462.Identity) -> NoneType:
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-    return None
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-class NonDynamicallyQuantizableLinear(Module):
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+++ /dev/null
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-class Linear(Module):
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-    return input
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-class NonDynamicallyQuantizableLinear(Module):
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+++ /dev/null
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-class Linear(Module):
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-class Identity(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity) -> NoneType:
-    return None
-  def forward1(self: __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity) -> NoneType:
-    return None
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-    return None
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity) -> NoneType:
-    return None
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity) -> NoneType:
-    return None
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity) -> NoneType:
-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity) -> NoneType:
-    return None
-  def forward7(self: __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity) -> NoneType:
-    return None
-  def forward8(self: __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity) -> NoneType:
-    return None
-  def forward9(self: __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity) -> NoneType:
-    return None
-  def forward10(self: __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity) -> NoneType:
-    return None
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-    return None
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-    return None
-  def forward13(self: __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity) -> NoneType:
-    return None
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_494.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_494.py.debug_pkl
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+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_495.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_495.py
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_495.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class NonDynamicallyQuantizableLinear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_495.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_495.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_499.py
+++ /dev/null
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-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward1(self: __torch__.torch.nn.modules.linear.___torch_mangle_499.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_499.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_499.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_499.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_499.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_499.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_499.py.debug_pkl
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+++ /dev/null
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_501.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_510.py
+++ /dev/null
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-class Identity(Module):
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_510.Identity) -> NoneType:
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-    return None
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-    return None
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_510.Identity) -> NoneType:
-    return None
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_510.Identity) -> NoneType:
-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_510.Identity) -> NoneType:
-    return None
-  def forward7(self: __torch__.torch.nn.modules.linear.___torch_mangle_510.Identity) -> NoneType:
-    return None
-  def forward8(self: __torch__.torch.nn.modules.linear.___torch_mangle_510.Identity) -> NoneType:
-    return None
-  def forward9(self: __torch__.torch.nn.modules.linear.___torch_mangle_510.Identity) -> NoneType:
-    return None
-  def forward10(self: __torch__.torch.nn.modules.linear.___torch_mangle_510.Identity) -> NoneType:
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-    return None
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-    return None
-  def forward13(self: __torch__.torch.nn.modules.linear.___torch_mangle_510.Identity) -> NoneType:
-    return None
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_510.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_510.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_510.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_511.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class NonDynamicallyQuantizableLinear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_511.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_511.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_515.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
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-    return input
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-    return input
-  def forward2(self: __torch__.torch.nn.modules.linear.___torch_mangle_515.Linear,
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-    bias = self.bias
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-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_515.Linear,
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-    bias = self.bias
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-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_515.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_515.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_515.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_515.py.debug_pkl
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-class Linear(Module):
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_528.py
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-class Identity(Module):
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-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_528.Identity) -> NoneType:
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_528.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_528.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_696.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/linear/___torch_mangle_696.py.debug_pkl
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-class Linear(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_28.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_28.py.debug_pkl
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_34.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_34.py
deleted file mode 100644
index 3199ca73875d42d489e31f17b14f56f9118a40ce..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_34.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_34.GroupNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_34.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_34.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_34.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_343.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_343.py
deleted file mode 100644
index db87017d313c79742e0bb05c3c50dd56aebb8322..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_343.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_343.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input0 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input0
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_343.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input1 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input1
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_343.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input2 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input2
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_343.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input3 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input3
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_343.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input4 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input4
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_343.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input5 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input5
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_343.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input6 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_343.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_343.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_346.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_346.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_346.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_346.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_346.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_346.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_346.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_346.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_346.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_346.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_354.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input0 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input0
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input1 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input1
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input2 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input2
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input3 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input3
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input4 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input4
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input5 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input5
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input6 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_354.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_354.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_362.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return x
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_362.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_362.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_370.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_370.py
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_370.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_370.LayerNorm,
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-    return input0
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_370.LayerNorm,
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-    bias = self.bias
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-    return input1
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_370.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input2 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input2
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_370.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return input3
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_370.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    input4 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input4
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_370.LayerNorm,
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-    bias = self.bias
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-    return input5
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_370.LayerNorm,
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-    bias = self.bias
-    weight = self.weight
-    input6 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_370.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_370.py.debug_pkl
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+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
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-  __buffers__ = []
-  weight : Tensor
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-    return x
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_378.LayerNorm,
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-    return x
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_378.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_378.LayerNorm,
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-    return x
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_378.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return x
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_378.LayerNorm,
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-    bias = self.bias
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-    return x
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_378.LayerNorm,
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-    bias = self.bias
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-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_378.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_378.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_38.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_38.py
deleted file mode 100644
index 1da1917cfd7c3f844fdc27ce902afd8ce6e6e07d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_38.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_38.GroupNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_38.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_38.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_38.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_386.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_386.py
deleted file mode 100644
index 58fd8748802acebbabb97ed036e126a638888d59..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_386.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_386.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input0 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input0
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_386.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input1 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input1
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_386.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input2 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input2
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_386.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input3 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input3
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_386.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input4 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input4
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_386.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input5 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input5
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_386.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input6 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_386.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_386.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_394.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_394.py
deleted file mode 100644
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_394.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_394.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_394.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_394.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_394.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_394.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_394.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_394.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_394.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_394.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_402.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_402.py
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index 6536083f5225043db5f5274456588419a71d31c6..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_402.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input0 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input0
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input1 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input1
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input2 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input2
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input3 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input3
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input4 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input4
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input5 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input5
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input6 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_402.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_402.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_410.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_410.py
deleted file mode 100644
index e5bdb560eb0d97786d1579dc92586f189b874657..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_410.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_410.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_410.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_410.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_418.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_418.py
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_418.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_418.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input0 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input0
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_418.LayerNorm,
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-    bias = self.bias
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-    return input1
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_418.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input2 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input2
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_418.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input3 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input3
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_418.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input4 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input4
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_418.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    input5 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input5
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_418.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input6 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input6
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_418.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_418.py.debug_pkl
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+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_42.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_42.py
deleted file mode 100644
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_42.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_42.GroupNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_42.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_42.py.debug_pkl
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index f19cd1cde80c8924661b3d95f181164162eee2f4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_426.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_426.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_426.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_426.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return x
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_426.LayerNorm,
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-    return x
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_426.LayerNorm,
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-    return x
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_426.LayerNorm,
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-    return x
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_426.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_426.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_434.py
+++ /dev/null
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-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
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-    return input0
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_434.LayerNorm,
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-    return input1
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-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return input6
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-    return x
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-class GroupNorm(Module):
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-    return x
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-    return x
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-    return x
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_474.LayerNorm,
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-    bias = self.bias
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-    return x
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_474.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_474.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_474.py.debug_pkl
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index ab306ff6067278dff73cd5ea5ee506b98697125e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_482.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
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-    return input1
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-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return input2
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_482.LayerNorm,
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-    return input4
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-    return input5
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-    bias = self.bias
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-    return input6
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-    return x
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-    return x
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_891.py
+++ /dev/null
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
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-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_891.GroupNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_891.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_891.py.debug_pkl
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-class GroupNorm(Module):
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-    bias = self.bias
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_895.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_895.py.debug_pkl
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-class GroupNorm(Module):
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_948.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_948.GroupNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_948.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_948.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_95.py
+++ /dev/null
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_95.GroupNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_95.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/normalization/___torch_mangle_95.py.debug_pkl
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-class GroupNorm(Module):
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-    return input
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-    return input
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling.py
deleted file mode 100644
index a68199c770434ef1157a86d8221b625ee77f4d04..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class AdaptiveAvgPool1d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward1(self: __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward2(self: __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward4(self: __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_358.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_358.py
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_358.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class AdaptiveAvgPool1d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_358.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward1(self: __torch__.torch.nn.modules.pooling.___torch_mangle_358.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward2(self: __torch__.torch.nn.modules.pooling.___torch_mangle_358.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.___torch_mangle_358.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward4(self: __torch__.torch.nn.modules.pooling.___torch_mangle_358.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_358.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.___torch_mangle_358.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_358.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_358.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_374.py
+++ /dev/null
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-class AdaptiveAvgPool1d(Module):
-  __parameters__ = []
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_374.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
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-    return input
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-    argument_1: Tensor) -> Tensor:
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-    return input
-  def forward2(self: __torch__.torch.nn.modules.pooling.___torch_mangle_374.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.___torch_mangle_374.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward4(self: __torch__.torch.nn.modules.pooling.___torch_mangle_374.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_374.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.___torch_mangle_374.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_374.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_374.py.debug_pkl
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_390.py b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_390.py
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_390.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class AdaptiveAvgPool1d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_390.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward1(self: __torch__.torch.nn.modules.pooling.___torch_mangle_390.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward2(self: __torch__.torch.nn.modules.pooling.___torch_mangle_390.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.___torch_mangle_390.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward4(self: __torch__.torch.nn.modules.pooling.___torch_mangle_390.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_390.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.___torch_mangle_390.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_390.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_390.py.debug_pkl
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index 64d03ecffeabd15fab1900147f55669a00fc706f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_406.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class AdaptiveAvgPool1d(Module):
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
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-    return input
-  def forward1(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward2(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward4(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_406.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_406.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_422.py
+++ /dev/null
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-class AdaptiveAvgPool1d(Module):
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-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
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-    argument_1: Tensor) -> Tensor:
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-    return input
-  def forward2(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward4(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_422.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_422.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_438.py
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-class AdaptiveAvgPool1d(Module):
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-    argument_1: Tensor) -> Tensor:
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-    return input
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diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_438.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_438.py.debug_pkl
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-  def forward3(self: __torch__.torch.nn.modules.pooling.___torch_mangle_454.AdaptiveAvgPool1d,
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-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
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-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.___torch_mangle_454.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_454.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_454.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_470.py
+++ /dev/null
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-class AdaptiveAvgPool1d(Module):
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-    return input
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_486.py
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-class AdaptiveAvgPool1d(Module):
-  __parameters__ = []
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-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_764.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [128])
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_764.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_764.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_777.py
+++ /dev/null
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-class AdaptiveAvgPool1d(Module):
-  __parameters__ = []
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-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_777.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [128])
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_777.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_777.py.debug_pkl
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--- a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_790.py
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-class AdaptiveAvgPool1d(Module):
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-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_790.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [128])
-    return input
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-class AdaptiveAvgPool1d(Module):
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-    argument_1: Tensor) -> Tensor:
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-    return input
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-class AdaptiveAvgPool1d(Module):
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-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_816.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [128])
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_816.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_816.py.debug_pkl
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-class AdaptiveAvgPool1d(Module):
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-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_829.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [128])
-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_829.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_829.py.debug_pkl
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-class AdaptiveAvgPool1d(Module):
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-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_842.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
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-    return input
diff --git a/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_842.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/torch/nn/modules/pooling/___torch_mangle_842.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p2/code/__torch__/utils.py
+++ /dev/null
@@ -1,57 +0,0 @@
-class Encoder(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  enc : __torch__.torch.nn.modules.container.___torch_mangle_540.Sequential
-  def forward(self: __torch__.utils.Encoder,
-    input: Tensor) -> Tuple[Tensor, Tensor]:
-    enc = self.enc
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-    enc0 = self.enc
-    _0 = getattr(enc0, "0")
-    _2 = (_0).forward(input, )
-    return ((_1).forward(_2, ), _2)
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
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-  conv : __torch__.utils.BasicConv2d
-  def forward(self: __torch__.utils.ConvSC,
-    input: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(input, )
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_532.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_533.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_534.LeakyReLU
-  def forward(self: __torch__.utils.BasicConv2d,
-    input: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _1 = (norm).forward((conv).forward(input, ), )
-    return (act).forward(_1, )
-class Decoder(Module):
-  __parameters__ = []
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-  training : bool
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-  dec : __torch__.torch.nn.modules.container.___torch_mangle_1010.Sequential
-  readout : __torch__.torch.nn.modules.conv.___torch_mangle_1011.Conv2d
-  def forward(self: __torch__.utils.Decoder,
-    input: Tensor,
-    enc1: Tensor) -> Tensor:
-    readout = self.readout
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-    _0 = getattr(dec0, "0")
-    input0 = torch.cat([(_0).forward(input, ), enc1], 1)
-    _2 = (readout).forward((_1).forward(input0, ), )
-    return _2
diff --git a/xe_weights14/glonet_p2/code/__torch__/utils.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/utils.py.debug_pkl
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+++ /dev/null
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-class BasicConv2d(Module):
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-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_1001.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_1002.LeakyReLU
-  def forward(self: __torch__.utils.___torch_mangle_1003.BasicConv2d,
-    input: Tensor) -> Tensor:
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diff --git a/xe_weights14/glonet_p2/code/__torch__/utils/___torch_mangle_1003.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/utils/___torch_mangle_1003.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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-class ConvSC(Module):
-  __parameters__ = []
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-  def forward(self: __torch__.utils.___torch_mangle_1004.ConvSC,
-    input: Tensor) -> Tensor:
-    conv = self.conv
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diff --git a/xe_weights14/glonet_p2/code/__torch__/utils/___torch_mangle_1004.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/utils/___torch_mangle_1004.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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-class BasicConv2d(Module):
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-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_1006.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_1007.LeakyReLU
-  def forward(self: __torch__.utils.___torch_mangle_1008.BasicConv2d,
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-version https://git-lfs.github.com/spec/v1
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-class ConvSC(Module):
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-  def forward(self: __torch__.utils.___torch_mangle_1009.ConvSC,
-    input: Tensor) -> Tensor:
-    conv = self.conv
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diff --git a/xe_weights14/glonet_p2/code/__torch__/utils/___torch_mangle_1009.py.debug_pkl b/xe_weights14/glonet_p2/code/__torch__/utils/___torch_mangle_1009.py.debug_pkl
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+++ /dev/null
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-class BasicConv2d(Module):
-  __parameters__ = []
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-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_536.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_537.LeakyReLU
-  def forward(self: __torch__.utils.___torch_mangle_538.BasicConv2d,
-    argument_1: Tensor) -> Tensor:
-    act = self.act
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deleted file mode 100644
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diff --git a/xe_weights14/glonet_p3/.data/version b/xe_weights14/glonet_p3/.data/version
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diff --git a/xe_weights14/glonet_p3/byteorder b/xe_weights14/glonet_p3/byteorder
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diff --git a/xe_weights14/glonet_p3/code/__torch__.py b/xe_weights14/glonet_p3/code/__torch__.py
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+++ /dev/null
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-class SelectiveFilterLayer2(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.SelectiveFilterLayer2,
-    argument_1: Tensor) -> Tensor:
-    x_filtered = torch.clone(argument_1)
-    _0 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _1 = torch.slice(_0, 1, 0, 9223372036854775807)
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-    _4 = torch.clamp(_3, -3.0208167499999998, 10.036455200000001)
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-    _6 = torch.slice(_5, 1, 0, 9223372036854775807)
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-    _9 = torch.copy_(_8, _4)
-    _10 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _11 = torch.slice(_10, 1, 0, 9223372036854775807)
-    _12 = torch.slice(torch.select(_11, 2, 10), 2, 0, 9223372036854775807)
-    _13 = torch.slice(_12, 3, 0, 9223372036854775807)
-    _14 = torch.clamp(_13, -26.6851445, 12.80332698)
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-    _18 = torch.slice(_17, 3, 0, 9223372036854775807)
-    _19 = torch.copy_(_18, _14)
-    _20 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _21 = torch.slice(_20, 1, 0, 9223372036854775807)
-    _22 = torch.slice(torch.select(_21, 2, 1), 2, 0, 9223372036854775807)
-    _23 = torch.slice(_22, 3, 0, 9223372036854775807)
-    _24 = torch.clamp(_23, -2.32676046, 11.45260633)
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-    _26 = torch.slice(_25, 1, 0, 9223372036854775807)
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-    _28 = torch.slice(_27, 3, 0, 9223372036854775807)
-    _29 = torch.copy_(_28, _24)
-    _30 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _31 = torch.slice(_30, 1, 0, 9223372036854775807)
-    _32 = torch.slice(torch.select(_31, 2, 11), 2, 0, 9223372036854775807)
-    _33 = torch.slice(_32, 3, 0, 9223372036854775807)
-    _34 = torch.clamp(_33, -30.436869399999999, 12.74917331)
-    _35 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _36 = torch.slice(_35, 1, 0, 9223372036854775807)
-    _37 = torch.slice(torch.select(_36, 2, 11), 2, 0, 9223372036854775807)
-    _38 = torch.slice(_37, 3, 0, 9223372036854775807)
-    _39 = torch.copy_(_38, _34)
-    _40 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _41 = torch.slice(_40, 1, 0, 9223372036854775807)
-    _42 = torch.slice(torch.select(_41, 2, 2), 2, 0, 9223372036854775807)
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-    _44 = torch.clamp(_43, -2.3534519500000002, 13.588099079999999)
-    _45 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _46 = torch.slice(_45, 1, 0, 9223372036854775807)
-    _47 = torch.slice(torch.select(_46, 2, 2), 2, 0, 9223372036854775807)
-    _48 = torch.slice(_47, 3, 0, 9223372036854775807)
-    _49 = torch.copy_(_48, _44)
-    _50 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _51 = torch.slice(_50, 1, 0, 9223372036854775807)
-    _52 = torch.slice(torch.select(_51, 2, 12), 2, 0, 9223372036854775807)
-    _53 = torch.slice(_52, 3, 0, 9223372036854775807)
-    _54 = torch.clamp(_53, -49.812010399999998, 19.801711860000001)
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-    _59 = torch.copy_(_58, _54)
-    _60 = torch.slice(argument_1, 0, 0, 9223372036854775807)
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-    _64 = torch.clamp(_63, -2.6566027399999999, 14.149116319999999)
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-    _66 = torch.slice(_65, 1, 0, 9223372036854775807)
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-    _68 = torch.slice(_67, 3, 0, 9223372036854775807)
-    _69 = torch.copy_(_68, _64)
-    _70 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _71 = torch.slice(_70, 1, 0, 9223372036854775807)
-    _72 = torch.slice(torch.select(_71, 2, 13), 2, 0, 9223372036854775807)
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-    _74 = torch.clamp(_73, -89.020009799999997, 30.298318389999999)
-    _75 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _76 = torch.slice(_75, 1, 0, 9223372036854775807)
-    _77 = torch.slice(torch.select(_76, 2, 13), 2, 0, 9223372036854775807)
-    _78 = torch.slice(_77, 3, 0, 9223372036854775807)
-    _79 = torch.copy_(_78, _74)
-    _80 = torch.slice(argument_1, 0, 0, 9223372036854775807)
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-    _87 = torch.slice(torch.select(_86, 2, 4), 2, 0, 9223372036854775807)
-    _88 = torch.slice(_87, 3, 0, 9223372036854775807)
-    _89 = torch.copy_(_88, _84)
-    _90 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _91 = torch.slice(_90, 1, 0, 9223372036854775807)
-    _92 = torch.slice(torch.select(_91, 2, 14), 2, 0, 9223372036854775807)
-    _93 = torch.slice(_92, 3, 0, 9223372036854775807)
-    _94 = torch.clamp(_93, -3.7182411800000001, 36.8057838)
-    _95 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _96 = torch.slice(_95, 1, 0, 9223372036854775807)
-    _97 = torch.slice(torch.select(_96, 2, 14), 2, 0, 9223372036854775807)
-    _98 = torch.slice(_97, 3, 0, 9223372036854775807)
-    _99 = torch.copy_(_98, _94)
-    _100 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _101 = torch.slice(_100, 1, 0, 9223372036854775807)
-    _102 = torch.slice(torch.select(_101, 2, 5), 2, 0, 9223372036854775807)
-    _103 = torch.slice(_102, 3, 0, 9223372036854775807)
-    _104 = torch.clamp(_103, -2.8769678700000001, 16.857306739999999)
-    _105 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _106 = torch.slice(_105, 1, 0, 9223372036854775807)
-    _107 = torch.slice(torch.select(_106, 2, 5), 2, 0, 9223372036854775807)
-    _108 = torch.slice(_107, 3, 0, 9223372036854775807)
-    _109 = torch.copy_(_108, _104)
-    _110 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _111 = torch.slice(_110, 1, 0, 9223372036854775807)
-    _112 = torch.slice(torch.select(_111, 2, 15), 2, 0, 9223372036854775807)
-    _113 = torch.slice(_112, 3, 0, 9223372036854775807)
-    _114 = torch.clamp(_113, -3.93426046, 46.622034800000002)
-    _115 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _116 = torch.slice(_115, 1, 0, 9223372036854775807)
-    _117 = torch.slice(torch.select(_116, 2, 15), 2, 0, 9223372036854775807)
-    _118 = torch.slice(_117, 3, 0, 9223372036854775807)
-    _119 = torch.copy_(_118, _114)
-    _120 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _121 = torch.slice(_120, 1, 0, 9223372036854775807)
-    _122 = torch.slice(torch.select(_121, 2, 6), 2, 0, 9223372036854775807)
-    _123 = torch.slice(_122, 3, 0, 9223372036854775807)
-    _124 = torch.clamp(_123, -2.6880730800000001, 18.421139119999999)
-    _125 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _126 = torch.slice(_125, 1, 0, 9223372036854775807)
-    _127 = torch.slice(torch.select(_126, 2, 6), 2, 0, 9223372036854775807)
-    _128 = torch.slice(_127, 3, 0, 9223372036854775807)
-    _129 = torch.copy_(_128, _124)
-    _130 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _131 = torch.slice(_130, 1, 0, 9223372036854775807)
-    _132 = torch.slice(torch.select(_131, 2, 16), 2, 0, 9223372036854775807)
-    _133 = torch.slice(_132, 3, 0, 9223372036854775807)
-    _134 = torch.clamp(_133, -1.8956007699999999, 57.304723699999997)
-    _135 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _136 = torch.slice(_135, 1, 0, 9223372036854775807)
-    _137 = torch.slice(torch.select(_136, 2, 16), 2, 0, 9223372036854775807)
-    _138 = torch.slice(_137, 3, 0, 9223372036854775807)
-    _139 = torch.copy_(_138, _134)
-    _140 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _141 = torch.slice(_140, 1, 0, 9223372036854775807)
-    _142 = torch.slice(torch.select(_141, 2, 7), 2, 0, 9223372036854775807)
-    _143 = torch.slice(_142, 3, 0, 9223372036854775807)
-    _144 = torch.clamp(_143, -2.6075159600000002, 4.4335222700000001)
-    _145 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _146 = torch.slice(_145, 1, 0, 9223372036854775807)
-    _147 = torch.slice(torch.select(_146, 2, 7), 2, 0, 9223372036854775807)
-    _148 = torch.slice(_147, 3, 0, 9223372036854775807)
-    _149 = torch.copy_(_148, _144)
-    _150 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _151 = torch.slice(_150, 1, 0, 9223372036854775807)
-    _152 = torch.slice(torch.select(_151, 2, 17), 2, 0, 9223372036854775807)
-    _153 = torch.slice(_152, 3, 0, 9223372036854775807)
-    _154 = torch.clamp(_153, -2.3238606399999999, 4.1731540000000003)
-    _155 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _156 = torch.slice(_155, 1, 0, 9223372036854775807)
-    _157 = torch.slice(torch.select(_156, 2, 17), 2, 0, 9223372036854775807)
-    _158 = torch.slice(_157, 3, 0, 9223372036854775807)
-    _159 = torch.copy_(_158, _154)
-    _160 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _161 = torch.slice(_160, 1, 0, 9223372036854775807)
-    _162 = torch.slice(torch.select(_161, 2, 8), 2, 0, 9223372036854775807)
-    _163 = torch.slice(_162, 3, 0, 9223372036854775807)
-    _164 = torch.clamp(_163, -3.34896649, 4.86014064)
-    _165 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _166 = torch.slice(_165, 1, 0, 9223372036854775807)
-    _167 = torch.slice(torch.select(_166, 2, 8), 2, 0, 9223372036854775807)
-    _168 = torch.slice(_167, 3, 0, 9223372036854775807)
-    _169 = torch.copy_(_168, _164)
-    _170 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _171 = torch.slice(_170, 1, 0, 9223372036854775807)
-    _172 = torch.slice(torch.select(_171, 2, 18), 2, 0, 9223372036854775807)
-    _173 = torch.slice(_172, 3, 0, 9223372036854775807)
-    _174 = torch.clamp(_173, -1.64043655, 4.2743423600000003)
-    _175 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _176 = torch.slice(_175, 1, 0, 9223372036854775807)
-    _177 = torch.slice(torch.select(_176, 2, 18), 2, 0, 9223372036854775807)
-    _178 = torch.slice(_177, 3, 0, 9223372036854775807)
-    _179 = torch.copy_(_178, _174)
-    _180 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _181 = torch.slice(_180, 1, 0, 9223372036854775807)
-    _182 = torch.slice(torch.select(_181, 2, 9), 2, 0, 9223372036854775807)
-    _183 = torch.slice(_182, 3, 0, 9223372036854775807)
-    _184 = torch.clamp(_183, -1.9911485600000001, -0.14432796000000001)
-    _185 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _186 = torch.slice(_185, 1, 0, 9223372036854775807)
-    _187 = torch.slice(torch.select(_186, 2, 9), 2, 0, 9223372036854775807)
-    _188 = torch.slice(_187, 3, 0, 9223372036854775807)
-    _189 = torch.copy_(_188, _184)
-    _190 = torch.slice(argument_1, 0, 0, 9223372036854775807)
-    _191 = torch.slice(_190, 1, 0, 9223372036854775807)
-    _192 = torch.slice(torch.select(_191, 2, 19), 2, 0, 9223372036854775807)
-    _193 = torch.slice(_192, 3, 0, 9223372036854775807)
-    _194 = torch.clamp(_193, 0.013602837499999999, 0.71860109999999999)
-    _195 = torch.slice(x_filtered, 0, 0, 9223372036854775807)
-    _196 = torch.slice(_195, 1, 0, 9223372036854775807)
-    _197 = torch.slice(torch.select(_196, 2, 19), 2, 0, 9223372036854775807)
-    _198 = torch.slice(_197, 3, 0, 9223372036854775807)
-    _199 = torch.copy_(_198, _194)
-    return x_filtered
diff --git a/xe_weights14/glonet_p3/code/__torch__.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__.py.debug_pkl
deleted file mode 100644
index a006474354bebcddc0b2add5a4239d98387fbbb5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:57627cdf071ace123ce0f6fac313e0b9ed9cbb255d7cb68d7cdbe36390f9c5df
-size 59492
diff --git a/xe_weights14/glonet_p3/code/__torch__/NN.py b/xe_weights14/glonet_p3/code/__torch__/NN.py
deleted file mode 100644
index 43ced0fc9300ec2722fc5a0360a8054be85f7711..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NN.py
+++ /dev/null
@@ -1,146 +0,0 @@
-class Local_CNN_Branch(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  upconv : __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d
-  def forward(self: __torch__.NN.Local_CNN_Branch,
-    x_raw: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x_raw, 0))
-    _0 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x_raw, 1))
-    _1 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x_raw, 2))
-    _2 = int(C)
-    _3 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x_raw, 3))
-    _4 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x_raw, 4))
-    input = torch.view(x_raw, [-1, _3, _4, int(W)])
-    _5 = (upconv).forward(input, )
-    _6 = ops.prim.NumToTensor(torch.size(_5, 2))
-    _7 = int(_6)
-    _8 = ops.prim.NumToTensor(torch.size(_5, 3))
-    lc_features = torch.view(_5, [_0, _1, _2, _7, int(_8)])
-    return lc_features
-  def forward1(self: __torch__.NN.Local_CNN_Branch,
-    x: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _9 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _10 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _11 = int(C)
-    _12 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _13 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    input = torch.view(x, [-1, _12, _13, int(W)])
-    _14 = (upconv).forward1(input, )
-    _15 = ops.prim.NumToTensor(torch.size(_14, 2))
-    _16 = int(_15)
-    _17 = ops.prim.NumToTensor(torch.size(_14, 3))
-    lc_features = torch.view(_14, [_9, _10, _11, _16, int(_17)])
-    return lc_features
-  def forward2(self: __torch__.NN.Local_CNN_Branch,
-    x: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _18 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _19 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _20 = int(C)
-    _21 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _22 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    input = torch.view(x, [-1, _21, _22, int(W)])
-    _23 = (upconv).forward2(input, )
-    _24 = ops.prim.NumToTensor(torch.size(_23, 2))
-    _25 = int(_24)
-    _26 = ops.prim.NumToTensor(torch.size(_23, 3))
-    lc_features = torch.view(_23, [_18, _19, _20, _25, int(_26)])
-    return lc_features
-  def forward3(self: __torch__.NN.Local_CNN_Branch,
-    x: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _27 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _28 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _29 = int(C)
-    _30 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _31 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    input = torch.view(x, [-1, _30, _31, int(W)])
-    _32 = (upconv).forward3(input, )
-    _33 = ops.prim.NumToTensor(torch.size(_32, 2))
-    _34 = int(_33)
-    _35 = ops.prim.NumToTensor(torch.size(_32, 3))
-    lc_features = torch.view(_32, [_27, _28, _29, _34, int(_35)])
-    return lc_features
-  def forward4(self: __torch__.NN.Local_CNN_Branch,
-    x: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _36 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _37 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _38 = int(C)
-    _39 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _40 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    input = torch.view(x, [-1, _39, _40, int(W)])
-    _41 = (upconv).forward4(input, )
-    _42 = ops.prim.NumToTensor(torch.size(_41, 2))
-    _43 = int(_42)
-    _44 = ops.prim.NumToTensor(torch.size(_41, 3))
-    lc_features = torch.view(_41, [_36, _37, _38, _43, int(_44)])
-    return lc_features
-  def forward5(self: __torch__.NN.Local_CNN_Branch,
-    x: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _45 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _46 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _47 = int(C)
-    _48 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _49 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    input = torch.view(x, [-1, _48, _49, int(W)])
-    _50 = (upconv).forward5(input, )
-    _51 = ops.prim.NumToTensor(torch.size(_50, 2))
-    _52 = int(_51)
-    _53 = ops.prim.NumToTensor(torch.size(_50, 3))
-    lc_features = torch.view(_50, [_45, _46, _47, _52, int(_53)])
-    return lc_features
-  def forward6(self: __torch__.NN.Local_CNN_Branch,
-    x: Tensor) -> Tensor:
-    upconv = self.upconv
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _54 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _55 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _56 = int(C)
-    _57 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _58 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    input = torch.view(x, [-1, _57, _58, int(W)])
-    _59 = (upconv).forward6(input, )
-    _60 = ops.prim.NumToTensor(torch.size(_59, 2))
-    _61 = int(_60)
-    _62 = ops.prim.NumToTensor(torch.size(_59, 3))
-    lc_features = torch.view(_59, [_54, _55, _56, _61, int(_62)])
-    return lc_features
diff --git a/xe_weights14/glonet_p3/code/__torch__/NN.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NN.py.debug_pkl
deleted file mode 100644
index 4ec1019d2e13561fa0dc8954b8662d26e63d6b93..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NN.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7483a3fdc34f1b6afe0df633083a86d82e5b524c5abbbfa8c965ef6142876fc6
-size 13187
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO.py b/xe_weights14/glonet_p3/code/__torch__/NO.py
deleted file mode 100644
index 99fd63184ee59af1e0ba8bbfb863efadc8cfadb4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO.py
+++ /dev/null
@@ -1,1099 +0,0 @@
-class GF_Block(Module):
-  __parameters__ = ["pos_embed", ]
-  __buffers__ = []
-  pos_embed : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  patch_embed : __torch__.NO.PatchEmbed
-  pos_drop : __torch__.torch.nn.modules.dropout.Dropout
-  blks : __torch__.torch.nn.modules.container.ModuleList
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_522.LayerNorm
-  lproj : __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential
-  final_dropout : __torch__.torch.nn.modules.linear.___torch_mangle_528.Identity
-  def forward(self: __torch__.NO.GF_Block,
-    x_raw: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks0 = self.blks
-    _10 = getattr(blks0, "10")
-    blks1 = self.blks
-    _9 = getattr(blks1, "9")
-    blks2 = self.blks
-    _8 = getattr(blks2, "8")
-    blks3 = self.blks
-    _7 = getattr(blks3, "7")
-    blks4 = self.blks
-    _6 = getattr(blks4, "6")
-    blks5 = self.blks
-    _5 = getattr(blks5, "5")
-    blks6 = self.blks
-    _4 = getattr(blks6, "4")
-    blks7 = self.blks
-    _3 = getattr(blks7, "3")
-    blks8 = self.blks
-    _2 = getattr(blks8, "2")
-    blks9 = self.blks
-    _1 = getattr(blks9, "1")
-    blks10 = self.blks
-    _0 = getattr(blks10, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x_raw, 0))
-    _12 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x_raw, 1))
-    _13 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x_raw, 2))
-    _14 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x_raw, 3))
-    _15 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x_raw, 4))
-    _16 = int(W)
-    B0 = ops.prim.NumToTensor(torch.size(x_raw, 0))
-    T0 = ops.prim.NumToTensor(torch.size(x_raw, 1))
-    C0 = ops.prim.NumToTensor(torch.size(x_raw, 2))
-    _17 = int(C0)
-    H0 = ops.prim.NumToTensor(torch.size(x_raw, 3))
-    _18 = int(H0)
-    W0 = ops.prim.NumToTensor(torch.size(x_raw, 4))
-    _19 = int(W0)
-    _20 = [int(torch.mul(B0, T0)), _17, _18, _19]
-    x = torch.view(x_raw, _20)
-    input = torch.add((patch_embed).forward(x, ), pos_embed)
-    _21 = (_0).forward((pos_drop).forward(input, ), )
-    _22 = (_3).forward((_2).forward((_1).forward(_21, ), ), )
-    _23 = (_6).forward((_5).forward((_4).forward(_22, ), ), )
-    _24 = (_9).forward((_8).forward((_7).forward(_23, ), ), )
-    _25 = (_11).forward((_10).forward(_24, ), )
-    _26 = torch.transpose((norm).forward(_25, ), 1, 2)
-    x0 = torch.contiguous(_26)
-    input0 = torch.reshape(x0, [-1, 768, 42, 90])
-    _27 = (final_dropout).forward()
-    gf_features = torch.reshape((lproj).forward(input0, ), [_12, _13, _14, _15, _16])
-    return gf_features
-  def forward1(self: __torch__.NO.GF_Block,
-    x: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks11 = self.blks
-    _10 = getattr(blks11, "10")
-    blks12 = self.blks
-    _9 = getattr(blks12, "9")
-    blks13 = self.blks
-    _8 = getattr(blks13, "8")
-    blks14 = self.blks
-    _7 = getattr(blks14, "7")
-    blks15 = self.blks
-    _6 = getattr(blks15, "6")
-    blks16 = self.blks
-    _5 = getattr(blks16, "5")
-    blks17 = self.blks
-    _4 = getattr(blks17, "4")
-    blks18 = self.blks
-    _3 = getattr(blks18, "3")
-    blks19 = self.blks
-    _2 = getattr(blks19, "2")
-    blks20 = self.blks
-    _1 = getattr(blks20, "1")
-    blks21 = self.blks
-    _0 = getattr(blks21, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _16 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _17 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _18 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _19 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _20 = int(W)
-    B1 = ops.prim.NumToTensor(torch.size(x, 0))
-    T1 = ops.prim.NumToTensor(torch.size(x, 1))
-    C1 = ops.prim.NumToTensor(torch.size(x, 2))
-    _21 = int(C1)
-    H1 = ops.prim.NumToTensor(torch.size(x, 3))
-    _22 = int(H1)
-    W1 = ops.prim.NumToTensor(torch.size(x, 4))
-    _23 = int(W1)
-    _24 = [int(torch.mul(B1, T1)), _21, _22, _23]
-    x1 = torch.view(x, _24)
-    input = torch.add((patch_embed).forward1(x1, ), pos_embed)
-    _25 = (_0).forward1((pos_drop).forward1(input, ), )
-    _26 = (_2).forward1((_1).forward1(_25, ), )
-    _27 = (_4).forward1((_3).forward1(_26, ), )
-    _28 = (_6).forward1((_5).forward1(_27, ), )
-    _29 = (_8).forward1((_7).forward1(_28, ), )
-    _30 = (_10).forward1((_9).forward1(_29, ), )
-    _31 = (norm).forward1((_11).forward1(_30, ), )
-    x2 = torch.contiguous(torch.transpose(_31, 1, 2))
-    input1 = torch.reshape(x2, [-1, 768, 42, 90])
-    _32 = (final_dropout).forward1()
-    gf_features = torch.reshape((lproj).forward1(input1, ), [_16, _17, _18, _19, _20])
-    return gf_features
-  def forward2(self: __torch__.NO.GF_Block,
-    x: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks22 = self.blks
-    _10 = getattr(blks22, "10")
-    blks23 = self.blks
-    _9 = getattr(blks23, "9")
-    blks24 = self.blks
-    _8 = getattr(blks24, "8")
-    blks25 = self.blks
-    _7 = getattr(blks25, "7")
-    blks26 = self.blks
-    _6 = getattr(blks26, "6")
-    blks27 = self.blks
-    _5 = getattr(blks27, "5")
-    blks28 = self.blks
-    _4 = getattr(blks28, "4")
-    blks29 = self.blks
-    _3 = getattr(blks29, "3")
-    blks30 = self.blks
-    _2 = getattr(blks30, "2")
-    blks31 = self.blks
-    _1 = getattr(blks31, "1")
-    blks32 = self.blks
-    _0 = getattr(blks32, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _33 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _34 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _35 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _36 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _37 = int(W)
-    B2 = ops.prim.NumToTensor(torch.size(x, 0))
-    T2 = ops.prim.NumToTensor(torch.size(x, 1))
-    C2 = ops.prim.NumToTensor(torch.size(x, 2))
-    _38 = int(C2)
-    H2 = ops.prim.NumToTensor(torch.size(x, 3))
-    _39 = int(H2)
-    W2 = ops.prim.NumToTensor(torch.size(x, 4))
-    _40 = int(W2)
-    _41 = [int(torch.mul(B2, T2)), _38, _39, _40]
-    x3 = torch.view(x, _41)
-    input = torch.add((patch_embed).forward2(x3, ), pos_embed)
-    _42 = (_0).forward2((pos_drop).forward2(input, ), )
-    _43 = (_2).forward2((_1).forward2(_42, ), )
-    _44 = (_4).forward2((_3).forward2(_43, ), )
-    _45 = (_6).forward2((_5).forward2(_44, ), )
-    _46 = (_8).forward2((_7).forward2(_45, ), )
-    _47 = (_10).forward2((_9).forward2(_46, ), )
-    _48 = (norm).forward2((_11).forward2(_47, ), )
-    x4 = torch.contiguous(torch.transpose(_48, 1, 2))
-    input2 = torch.reshape(x4, [-1, 768, 42, 90])
-    _49 = (final_dropout).forward2()
-    gf_features = torch.reshape((lproj).forward2(input2, ), [_33, _34, _35, _36, _37])
-    return gf_features
-  def forward3(self: __torch__.NO.GF_Block,
-    x: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks33 = self.blks
-    _10 = getattr(blks33, "10")
-    blks34 = self.blks
-    _9 = getattr(blks34, "9")
-    blks35 = self.blks
-    _8 = getattr(blks35, "8")
-    blks36 = self.blks
-    _7 = getattr(blks36, "7")
-    blks37 = self.blks
-    _6 = getattr(blks37, "6")
-    blks38 = self.blks
-    _5 = getattr(blks38, "5")
-    blks39 = self.blks
-    _4 = getattr(blks39, "4")
-    blks40 = self.blks
-    _3 = getattr(blks40, "3")
-    blks41 = self.blks
-    _2 = getattr(blks41, "2")
-    blks42 = self.blks
-    _1 = getattr(blks42, "1")
-    blks43 = self.blks
-    _0 = getattr(blks43, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _50 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _51 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _52 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _53 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _54 = int(W)
-    B3 = ops.prim.NumToTensor(torch.size(x, 0))
-    T3 = ops.prim.NumToTensor(torch.size(x, 1))
-    C3 = ops.prim.NumToTensor(torch.size(x, 2))
-    _55 = int(C3)
-    H3 = ops.prim.NumToTensor(torch.size(x, 3))
-    _56 = int(H3)
-    W3 = ops.prim.NumToTensor(torch.size(x, 4))
-    _57 = int(W3)
-    _58 = [int(torch.mul(B3, T3)), _55, _56, _57]
-    x5 = torch.view(x, _58)
-    input = torch.add((patch_embed).forward3(x5, ), pos_embed)
-    _59 = (_0).forward3((pos_drop).forward3(input, ), )
-    _60 = (_2).forward3((_1).forward3(_59, ), )
-    _61 = (_4).forward3((_3).forward3(_60, ), )
-    _62 = (_6).forward3((_5).forward3(_61, ), )
-    _63 = (_8).forward3((_7).forward3(_62, ), )
-    _64 = (_10).forward3((_9).forward3(_63, ), )
-    _65 = (norm).forward3((_11).forward3(_64, ), )
-    x6 = torch.contiguous(torch.transpose(_65, 1, 2))
-    input3 = torch.reshape(x6, [-1, 768, 42, 90])
-    _66 = (final_dropout).forward3()
-    gf_features = torch.reshape((lproj).forward3(input3, ), [_50, _51, _52, _53, _54])
-    return gf_features
-  def forward4(self: __torch__.NO.GF_Block,
-    x: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks44 = self.blks
-    _10 = getattr(blks44, "10")
-    blks45 = self.blks
-    _9 = getattr(blks45, "9")
-    blks46 = self.blks
-    _8 = getattr(blks46, "8")
-    blks47 = self.blks
-    _7 = getattr(blks47, "7")
-    blks48 = self.blks
-    _6 = getattr(blks48, "6")
-    blks49 = self.blks
-    _5 = getattr(blks49, "5")
-    blks50 = self.blks
-    _4 = getattr(blks50, "4")
-    blks51 = self.blks
-    _3 = getattr(blks51, "3")
-    blks52 = self.blks
-    _2 = getattr(blks52, "2")
-    blks53 = self.blks
-    _1 = getattr(blks53, "1")
-    blks54 = self.blks
-    _0 = getattr(blks54, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _67 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _68 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _69 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _70 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _71 = int(W)
-    B4 = ops.prim.NumToTensor(torch.size(x, 0))
-    T4 = ops.prim.NumToTensor(torch.size(x, 1))
-    C4 = ops.prim.NumToTensor(torch.size(x, 2))
-    _72 = int(C4)
-    H4 = ops.prim.NumToTensor(torch.size(x, 3))
-    _73 = int(H4)
-    W4 = ops.prim.NumToTensor(torch.size(x, 4))
-    _74 = int(W4)
-    _75 = [int(torch.mul(B4, T4)), _72, _73, _74]
-    x7 = torch.view(x, _75)
-    input = torch.add((patch_embed).forward4(x7, ), pos_embed)
-    _76 = (_0).forward4((pos_drop).forward4(input, ), )
-    _77 = (_2).forward4((_1).forward4(_76, ), )
-    _78 = (_4).forward4((_3).forward4(_77, ), )
-    _79 = (_6).forward4((_5).forward4(_78, ), )
-    _80 = (_8).forward4((_7).forward4(_79, ), )
-    _81 = (_10).forward4((_9).forward4(_80, ), )
-    _82 = (norm).forward4((_11).forward4(_81, ), )
-    x8 = torch.contiguous(torch.transpose(_82, 1, 2))
-    input4 = torch.reshape(x8, [-1, 768, 42, 90])
-    _83 = (final_dropout).forward4()
-    gf_features = torch.reshape((lproj).forward4(input4, ), [_67, _68, _69, _70, _71])
-    return gf_features
-  def forward5(self: __torch__.NO.GF_Block,
-    x: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks55 = self.blks
-    _10 = getattr(blks55, "10")
-    blks56 = self.blks
-    _9 = getattr(blks56, "9")
-    blks57 = self.blks
-    _8 = getattr(blks57, "8")
-    blks58 = self.blks
-    _7 = getattr(blks58, "7")
-    blks59 = self.blks
-    _6 = getattr(blks59, "6")
-    blks60 = self.blks
-    _5 = getattr(blks60, "5")
-    blks61 = self.blks
-    _4 = getattr(blks61, "4")
-    blks62 = self.blks
-    _3 = getattr(blks62, "3")
-    blks63 = self.blks
-    _2 = getattr(blks63, "2")
-    blks64 = self.blks
-    _1 = getattr(blks64, "1")
-    blks65 = self.blks
-    _0 = getattr(blks65, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _84 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _85 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _86 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _87 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _88 = int(W)
-    B5 = ops.prim.NumToTensor(torch.size(x, 0))
-    T5 = ops.prim.NumToTensor(torch.size(x, 1))
-    C5 = ops.prim.NumToTensor(torch.size(x, 2))
-    _89 = int(C5)
-    H5 = ops.prim.NumToTensor(torch.size(x, 3))
-    _90 = int(H5)
-    W5 = ops.prim.NumToTensor(torch.size(x, 4))
-    _91 = int(W5)
-    _92 = [int(torch.mul(B5, T5)), _89, _90, _91]
-    x9 = torch.view(x, _92)
-    input = torch.add((patch_embed).forward5(x9, ), pos_embed)
-    _93 = (_0).forward5((pos_drop).forward5(input, ), )
-    _94 = (_2).forward5((_1).forward5(_93, ), )
-    _95 = (_4).forward5((_3).forward5(_94, ), )
-    _96 = (_6).forward5((_5).forward5(_95, ), )
-    _97 = (_8).forward5((_7).forward5(_96, ), )
-    _98 = (_10).forward5((_9).forward5(_97, ), )
-    _99 = (norm).forward5((_11).forward5(_98, ), )
-    x10 = torch.contiguous(torch.transpose(_99, 1, 2))
-    input5 = torch.reshape(x10, [-1, 768, 42, 90])
-    _100 = (final_dropout).forward5()
-    gf_features = torch.reshape((lproj).forward5(input5, ), [_84, _85, _86, _87, _88])
-    return gf_features
-  def forward6(self: __torch__.NO.GF_Block,
-    x: Tensor) -> Tensor:
-    lproj = self.lproj
-    final_dropout = self.final_dropout
-    norm = self.norm
-    blks = self.blks
-    _11 = getattr(blks, "11")
-    blks66 = self.blks
-    _10 = getattr(blks66, "10")
-    blks67 = self.blks
-    _9 = getattr(blks67, "9")
-    blks68 = self.blks
-    _8 = getattr(blks68, "8")
-    blks69 = self.blks
-    _7 = getattr(blks69, "7")
-    blks70 = self.blks
-    _6 = getattr(blks70, "6")
-    blks71 = self.blks
-    _5 = getattr(blks71, "5")
-    blks72 = self.blks
-    _4 = getattr(blks72, "4")
-    blks73 = self.blks
-    _3 = getattr(blks73, "3")
-    blks74 = self.blks
-    _2 = getattr(blks74, "2")
-    blks75 = self.blks
-    _1 = getattr(blks75, "1")
-    blks76 = self.blks
-    _0 = getattr(blks76, "0")
-    pos_drop = self.pos_drop
-    pos_embed = self.pos_embed
-    patch_embed = self.patch_embed
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _101 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _102 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _103 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _104 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _105 = int(W)
-    B6 = ops.prim.NumToTensor(torch.size(x, 0))
-    T6 = ops.prim.NumToTensor(torch.size(x, 1))
-    C6 = ops.prim.NumToTensor(torch.size(x, 2))
-    _106 = int(C6)
-    H6 = ops.prim.NumToTensor(torch.size(x, 3))
-    _107 = int(H6)
-    W6 = ops.prim.NumToTensor(torch.size(x, 4))
-    _108 = int(W6)
-    _109 = [int(torch.mul(B6, T6)), _106, _107, _108]
-    x11 = torch.view(x, _109)
-    input = torch.add((patch_embed).forward6(x11, ), pos_embed)
-    _110 = (_0).forward6((pos_drop).forward6(input, ), )
-    _111 = (_2).forward6((_1).forward6(_110, ), )
-    _112 = (_4).forward6((_3).forward6(_111, ), )
-    _113 = (_6).forward6((_5).forward6(_112, ), )
-    _114 = (_8).forward6((_7).forward6(_113, ), )
-    _115 = (_10).forward6((_9).forward6(_114, ), )
-    _116 = (norm).forward6((_11).forward6(_115, ), )
-    x12 = torch.contiguous(torch.transpose(_116, 1, 2))
-    input6 = torch.reshape(x12, [-1, 768, 42, 90])
-    _117 = (final_dropout).forward6()
-    gf_features = torch.reshape((lproj).forward6(input6, ), [_101, _102, _103, _104, _105])
-    return gf_features
-class PatchEmbed(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  projection : __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d
-  norm : __torch__.torch.nn.modules.linear.Identity
-  def forward(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _118 = torch.flatten((projection).forward(x, ), 2)
-    x13 = torch.contiguous(torch.transpose(_118, 1, 2))
-    _119 = (norm).forward()
-    return x13
-  def forward1(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _120 = torch.flatten((projection).forward1(x, ), 2)
-    x14 = torch.contiguous(torch.transpose(_120, 1, 2))
-    _121 = (norm).forward1()
-    return x14
-  def forward2(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _122 = torch.flatten((projection).forward2(x, ), 2)
-    x15 = torch.contiguous(torch.transpose(_122, 1, 2))
-    _123 = (norm).forward2()
-    return x15
-  def forward3(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _124 = torch.flatten((projection).forward3(x, ), 2)
-    x16 = torch.contiguous(torch.transpose(_124, 1, 2))
-    _125 = (norm).forward3()
-    return x16
-  def forward4(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _126 = torch.flatten((projection).forward4(x, ), 2)
-    x17 = torch.contiguous(torch.transpose(_126, 1, 2))
-    _127 = (norm).forward4()
-    return x17
-  def forward5(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _128 = torch.flatten((projection).forward5(x, ), 2)
-    x18 = torch.contiguous(torch.transpose(_128, 1, 2))
-    _129 = (norm).forward5()
-    return x18
-  def forward6(self: __torch__.NO.PatchEmbed,
-    x: Tensor) -> Tensor:
-    norm = self.norm
-    projection = self.projection
-    _130 = torch.flatten((projection).forward6(x, ), 2)
-    x19 = torch.contiguous(torch.transpose(_130, 1, 2))
-    _131 = (norm).forward6()
-    return x19
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.LayerNorm
-  filter : __torch__.NO.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_342.Identity
-  attn : __torch__.NO.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_343.LayerNorm
-  mlp : __torch__.NO.Mlp
-  def forward(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _132 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _133 = (drop_path).forward()
-    input = torch.add(argument_1, _132)
-    _134 = (mlp).forward((normlayer2).forward(input, ), )
-    _135 = (drop_path).forward1()
-    return torch.add(input, _134)
-  def forward1(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _136 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _137 = (drop_path).forward2()
-    input = torch.add(argument_1, _136)
-    _138 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _139 = (drop_path).forward3()
-    return torch.add(input, _138)
-  def forward2(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _140 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _141 = (drop_path).forward4()
-    input = torch.add(argument_1, _140)
-    _142 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _143 = (drop_path).forward5()
-    return torch.add(input, _142)
-  def forward3(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _144 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _145 = (drop_path).forward6()
-    input = torch.add(argument_1, _144)
-    _146 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _147 = (drop_path).forward7()
-    return torch.add(input, _146)
-  def forward4(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _148 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _149 = (drop_path).forward8()
-    input = torch.add(argument_1, _148)
-    _150 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _151 = (drop_path).forward9()
-    return torch.add(input, _150)
-  def forward5(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _152 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _153 = (drop_path).forward10()
-    input = torch.add(argument_1, _152)
-    _154 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _155 = (drop_path).forward11()
-    return torch.add(input, _154)
-  def forward6(self: __torch__.NO.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _156 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _157 = (drop_path).forward12()
-    input = torch.add(argument_1, _156)
-    _158 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _159 = (drop_path).forward13()
-    return torch.add(input, _158)
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.ReLU
-  bias : __torch__.torch.nn.modules.conv.Conv1d
-  def forward(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _160 = int(B)
-    _161 = int(B)
-    _162 = int(B)
-    _163 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _164 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _165 = int(C)
-    _166 = int(C)
-    _167 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_167)
-    _168 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_168)
-    x = torch.reshape(argument_1, [_163, 42, 90, _166])
-    x20 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _169 = ops.prim.NumToTensor(torch.size(x20, 1))
-    _170 = int(_169)
-    _171 = ops.prim.NumToTensor(torch.size(x20, 2))
-    x21 = torch.reshape(x20, [_162, _170, int(_171), 2, 384])
-    _172 = [torch.real(x21), torch.select(w1, 0, 0)]
-    _173 = torch.einsum("...bd, bdk->...bk", _172)
-    _174 = [torch.imag(x21), torch.select(w1, 0, 1)]
-    _175 = torch.einsum("...bd, bdk->...bk", _174)
-    input7 = torch.add(torch.sub(_173, _175), torch.select(b1, 0, 0))
-    _176 = torch.relu_(input7)
-    _177 = [torch.real(x21), torch.select(w1, 0, 1)]
-    _178 = torch.einsum("...bd, bdk->...bk", _177)
-    _179 = [torch.imag(x21), torch.select(w1, 0, 0)]
-    _180 = torch.einsum("...bd, bdk->...bk", _179)
-    input8 = torch.add(torch.add(_178, _180), torch.select(b1, 0, 1))
-    _181 = torch.relu_(input8)
-    _182 = torch.einsum("...bd, bdk->...bk", [_176, torch.select(w2, 0, 0)])
-    _183 = torch.einsum("...bd, bdk->...bk", [_181, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_182, _183), torch.select(b2, 0, 0))
-    _184 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _185 = torch.einsum("...bd, bdk->...bk", [_181, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_184, _185), torch.select(b2, 0, 1))
-    x22 = torch.stack([x_real, x_imag], -1)
-    x23 = torch.view_as_complex(x22)
-    _186 = ops.prim.NumToTensor(torch.size(x23, 1))
-    _187 = int(_186)
-    _188 = ops.prim.NumToTensor(torch.size(x23, 2))
-    x24 = torch.reshape(x23, [_161, _187, int(_188), 768])
-    x25 = torch.fft_irfft2(x24, [42, 90], [1, 2], "ortho")
-    x26 = torch.reshape(x25, [_160, _164, _165])
-    return torch.add(x26, bias0)
-  def forward1(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _189 = int(B)
-    _190 = int(B)
-    _191 = int(B)
-    _192 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _193 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _194 = int(C)
-    _195 = int(C)
-    _196 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_196)
-    _197 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_197)
-    x = torch.reshape(argument_1, [_192, 42, 90, _195])
-    x27 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _198 = ops.prim.NumToTensor(torch.size(x27, 1))
-    _199 = int(_198)
-    _200 = ops.prim.NumToTensor(torch.size(x27, 2))
-    x28 = torch.reshape(x27, [_191, _199, int(_200), 2, 384])
-    _201 = [torch.real(x28), torch.select(w1, 0, 0)]
-    _202 = torch.einsum("...bd, bdk->...bk", _201)
-    _203 = [torch.imag(x28), torch.select(w1, 0, 1)]
-    _204 = torch.einsum("...bd, bdk->...bk", _203)
-    input9 = torch.add(torch.sub(_202, _204), torch.select(b1, 0, 0))
-    _205 = torch.relu_(input9)
-    _206 = [torch.real(x28), torch.select(w1, 0, 1)]
-    _207 = torch.einsum("...bd, bdk->...bk", _206)
-    _208 = [torch.imag(x28), torch.select(w1, 0, 0)]
-    _209 = torch.einsum("...bd, bdk->...bk", _208)
-    input10 = torch.add(torch.add(_207, _209), torch.select(b1, 0, 1))
-    _210 = torch.relu_(input10)
-    _211 = torch.einsum("...bd, bdk->...bk", [_205, torch.select(w2, 0, 0)])
-    _212 = torch.einsum("...bd, bdk->...bk", [_210, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_211, _212), torch.select(b2, 0, 0))
-    _213 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _214 = torch.einsum("...bd, bdk->...bk", [_210, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_213, _214), torch.select(b2, 0, 1))
-    x29 = torch.stack([x_real, x_imag], -1)
-    x30 = torch.view_as_complex(x29)
-    _215 = ops.prim.NumToTensor(torch.size(x30, 1))
-    _216 = int(_215)
-    _217 = ops.prim.NumToTensor(torch.size(x30, 2))
-    x31 = torch.reshape(x30, [_190, _216, int(_217), 768])
-    x32 = torch.fft_irfft2(x31, [42, 90], [1, 2], "ortho")
-    x33 = torch.reshape(x32, [_189, _193, _194])
-    return torch.add(x33, bias1)
-  def forward2(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _218 = int(B)
-    _219 = int(B)
-    _220 = int(B)
-    _221 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _222 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _223 = int(C)
-    _224 = int(C)
-    _225 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_225)
-    _226 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_226)
-    x = torch.reshape(argument_1, [_221, 42, 90, _224])
-    x34 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _227 = ops.prim.NumToTensor(torch.size(x34, 1))
-    _228 = int(_227)
-    _229 = ops.prim.NumToTensor(torch.size(x34, 2))
-    x35 = torch.reshape(x34, [_220, _228, int(_229), 2, 384])
-    _230 = [torch.real(x35), torch.select(w1, 0, 0)]
-    _231 = torch.einsum("...bd, bdk->...bk", _230)
-    _232 = [torch.imag(x35), torch.select(w1, 0, 1)]
-    _233 = torch.einsum("...bd, bdk->...bk", _232)
-    input11 = torch.add(torch.sub(_231, _233), torch.select(b1, 0, 0))
-    _234 = torch.relu_(input11)
-    _235 = [torch.real(x35), torch.select(w1, 0, 1)]
-    _236 = torch.einsum("...bd, bdk->...bk", _235)
-    _237 = [torch.imag(x35), torch.select(w1, 0, 0)]
-    _238 = torch.einsum("...bd, bdk->...bk", _237)
-    input12 = torch.add(torch.add(_236, _238), torch.select(b1, 0, 1))
-    _239 = torch.relu_(input12)
-    _240 = torch.einsum("...bd, bdk->...bk", [_234, torch.select(w2, 0, 0)])
-    _241 = torch.einsum("...bd, bdk->...bk", [_239, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_240, _241), torch.select(b2, 0, 0))
-    _242 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _243 = torch.einsum("...bd, bdk->...bk", [_239, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_242, _243), torch.select(b2, 0, 1))
-    x36 = torch.stack([x_real, x_imag], -1)
-    x37 = torch.view_as_complex(x36)
-    _244 = ops.prim.NumToTensor(torch.size(x37, 1))
-    _245 = int(_244)
-    _246 = ops.prim.NumToTensor(torch.size(x37, 2))
-    x38 = torch.reshape(x37, [_219, _245, int(_246), 768])
-    x39 = torch.fft_irfft2(x38, [42, 90], [1, 2], "ortho")
-    x40 = torch.reshape(x39, [_218, _222, _223])
-    return torch.add(x40, bias2)
-  def forward3(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _247 = int(B)
-    _248 = int(B)
-    _249 = int(B)
-    _250 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _251 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _252 = int(C)
-    _253 = int(C)
-    _254 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_254)
-    _255 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_255)
-    x = torch.reshape(argument_1, [_250, 42, 90, _253])
-    x41 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _256 = ops.prim.NumToTensor(torch.size(x41, 1))
-    _257 = int(_256)
-    _258 = ops.prim.NumToTensor(torch.size(x41, 2))
-    x42 = torch.reshape(x41, [_249, _257, int(_258), 2, 384])
-    _259 = [torch.real(x42), torch.select(w1, 0, 0)]
-    _260 = torch.einsum("...bd, bdk->...bk", _259)
-    _261 = [torch.imag(x42), torch.select(w1, 0, 1)]
-    _262 = torch.einsum("...bd, bdk->...bk", _261)
-    input13 = torch.add(torch.sub(_260, _262), torch.select(b1, 0, 0))
-    _263 = torch.relu_(input13)
-    _264 = [torch.real(x42), torch.select(w1, 0, 1)]
-    _265 = torch.einsum("...bd, bdk->...bk", _264)
-    _266 = [torch.imag(x42), torch.select(w1, 0, 0)]
-    _267 = torch.einsum("...bd, bdk->...bk", _266)
-    input14 = torch.add(torch.add(_265, _267), torch.select(b1, 0, 1))
-    _268 = torch.relu_(input14)
-    _269 = torch.einsum("...bd, bdk->...bk", [_263, torch.select(w2, 0, 0)])
-    _270 = torch.einsum("...bd, bdk->...bk", [_268, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_269, _270), torch.select(b2, 0, 0))
-    _271 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _272 = torch.einsum("...bd, bdk->...bk", [_268, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_271, _272), torch.select(b2, 0, 1))
-    x43 = torch.stack([x_real, x_imag], -1)
-    x44 = torch.view_as_complex(x43)
-    _273 = ops.prim.NumToTensor(torch.size(x44, 1))
-    _274 = int(_273)
-    _275 = ops.prim.NumToTensor(torch.size(x44, 2))
-    x45 = torch.reshape(x44, [_248, _274, int(_275), 768])
-    x46 = torch.fft_irfft2(x45, [42, 90], [1, 2], "ortho")
-    x47 = torch.reshape(x46, [_247, _251, _252])
-    return torch.add(x47, bias3)
-  def forward4(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _276 = int(B)
-    _277 = int(B)
-    _278 = int(B)
-    _279 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _280 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _281 = int(C)
-    _282 = int(C)
-    _283 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_283)
-    _284 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_284)
-    x = torch.reshape(argument_1, [_279, 42, 90, _282])
-    x48 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _285 = ops.prim.NumToTensor(torch.size(x48, 1))
-    _286 = int(_285)
-    _287 = ops.prim.NumToTensor(torch.size(x48, 2))
-    x49 = torch.reshape(x48, [_278, _286, int(_287), 2, 384])
-    _288 = [torch.real(x49), torch.select(w1, 0, 0)]
-    _289 = torch.einsum("...bd, bdk->...bk", _288)
-    _290 = [torch.imag(x49), torch.select(w1, 0, 1)]
-    _291 = torch.einsum("...bd, bdk->...bk", _290)
-    input15 = torch.add(torch.sub(_289, _291), torch.select(b1, 0, 0))
-    _292 = torch.relu_(input15)
-    _293 = [torch.real(x49), torch.select(w1, 0, 1)]
-    _294 = torch.einsum("...bd, bdk->...bk", _293)
-    _295 = [torch.imag(x49), torch.select(w1, 0, 0)]
-    _296 = torch.einsum("...bd, bdk->...bk", _295)
-    input16 = torch.add(torch.add(_294, _296), torch.select(b1, 0, 1))
-    _297 = torch.relu_(input16)
-    _298 = torch.einsum("...bd, bdk->...bk", [_292, torch.select(w2, 0, 0)])
-    _299 = torch.einsum("...bd, bdk->...bk", [_297, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_298, _299), torch.select(b2, 0, 0))
-    _300 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _301 = torch.einsum("...bd, bdk->...bk", [_297, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_300, _301), torch.select(b2, 0, 1))
-    x50 = torch.stack([x_real, x_imag], -1)
-    x51 = torch.view_as_complex(x50)
-    _302 = ops.prim.NumToTensor(torch.size(x51, 1))
-    _303 = int(_302)
-    _304 = ops.prim.NumToTensor(torch.size(x51, 2))
-    x52 = torch.reshape(x51, [_277, _303, int(_304), 768])
-    x53 = torch.fft_irfft2(x52, [42, 90], [1, 2], "ortho")
-    x54 = torch.reshape(x53, [_276, _280, _281])
-    return torch.add(x54, bias4)
-  def forward5(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _305 = int(B)
-    _306 = int(B)
-    _307 = int(B)
-    _308 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _309 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _310 = int(C)
-    _311 = int(C)
-    _312 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_312)
-    _313 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_313)
-    x = torch.reshape(argument_1, [_308, 42, 90, _311])
-    x55 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _314 = ops.prim.NumToTensor(torch.size(x55, 1))
-    _315 = int(_314)
-    _316 = ops.prim.NumToTensor(torch.size(x55, 2))
-    x56 = torch.reshape(x55, [_307, _315, int(_316), 2, 384])
-    _317 = [torch.real(x56), torch.select(w1, 0, 0)]
-    _318 = torch.einsum("...bd, bdk->...bk", _317)
-    _319 = [torch.imag(x56), torch.select(w1, 0, 1)]
-    _320 = torch.einsum("...bd, bdk->...bk", _319)
-    input17 = torch.add(torch.sub(_318, _320), torch.select(b1, 0, 0))
-    _321 = torch.relu_(input17)
-    _322 = [torch.real(x56), torch.select(w1, 0, 1)]
-    _323 = torch.einsum("...bd, bdk->...bk", _322)
-    _324 = [torch.imag(x56), torch.select(w1, 0, 0)]
-    _325 = torch.einsum("...bd, bdk->...bk", _324)
-    input18 = torch.add(torch.add(_323, _325), torch.select(b1, 0, 1))
-    _326 = torch.relu_(input18)
-    _327 = torch.einsum("...bd, bdk->...bk", [_321, torch.select(w2, 0, 0)])
-    _328 = torch.einsum("...bd, bdk->...bk", [_326, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_327, _328), torch.select(b2, 0, 0))
-    _329 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _330 = torch.einsum("...bd, bdk->...bk", [_326, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_329, _330), torch.select(b2, 0, 1))
-    x57 = torch.stack([x_real, x_imag], -1)
-    x58 = torch.view_as_complex(x57)
-    _331 = ops.prim.NumToTensor(torch.size(x58, 1))
-    _332 = int(_331)
-    _333 = ops.prim.NumToTensor(torch.size(x58, 2))
-    x59 = torch.reshape(x58, [_306, _332, int(_333), 768])
-    x60 = torch.fft_irfft2(x59, [42, 90], [1, 2], "ortho")
-    x61 = torch.reshape(x60, [_305, _309, _310])
-    return torch.add(x61, bias5)
-  def forward6(self: __torch__.NO.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _334 = int(B)
-    _335 = int(B)
-    _336 = int(B)
-    _337 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _338 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _339 = int(C)
-    _340 = int(C)
-    _341 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_341)
-    _342 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_342)
-    x = torch.reshape(argument_1, [_337, 42, 90, _340])
-    x62 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _343 = ops.prim.NumToTensor(torch.size(x62, 1))
-    _344 = int(_343)
-    _345 = ops.prim.NumToTensor(torch.size(x62, 2))
-    x63 = torch.reshape(x62, [_336, _344, int(_345), 2, 384])
-    _346 = [torch.real(x63), torch.select(w1, 0, 0)]
-    _347 = torch.einsum("...bd, bdk->...bk", _346)
-    _348 = [torch.imag(x63), torch.select(w1, 0, 1)]
-    _349 = torch.einsum("...bd, bdk->...bk", _348)
-    input19 = torch.add(torch.sub(_347, _349), torch.select(b1, 0, 0))
-    _350 = torch.relu_(input19)
-    _351 = [torch.real(x63), torch.select(w1, 0, 1)]
-    _352 = torch.einsum("...bd, bdk->...bk", _351)
-    _353 = [torch.imag(x63), torch.select(w1, 0, 0)]
-    _354 = torch.einsum("...bd, bdk->...bk", _353)
-    input20 = torch.add(torch.add(_352, _354), torch.select(b1, 0, 1))
-    _355 = torch.relu_(input20)
-    _356 = torch.einsum("...bd, bdk->...bk", [_350, torch.select(w2, 0, 0)])
-    _357 = torch.einsum("...bd, bdk->...bk", [_355, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_356, _357), torch.select(b2, 0, 0))
-    _358 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _359 = torch.einsum("...bd, bdk->...bk", [_355, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_358, _359), torch.select(b2, 0, 1))
-    x64 = torch.stack([x_real, x_imag], -1)
-    x65 = torch.view_as_complex(x64)
-    _360 = ops.prim.NumToTensor(torch.size(x65, 1))
-    _361 = int(_360)
-    _362 = ops.prim.NumToTensor(torch.size(x65, 2))
-    x66 = torch.reshape(x65, [_335, _361, int(_362), 768])
-    x67 = torch.fft_irfft2(x66, [42, 90], [1, 2], "ortho")
-    x68 = torch.reshape(x67, [_334, _338, _339])
-    return torch.add(x68, bias6)
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.MultiheadAttention
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.Linear
-  act : __torch__.torch.nn.modules.activation.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_344.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout
-  def forward(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _363 = (act).forward((fc1).forward(argument_1, ), )
-    _364 = (fc3).forward((drop).forward(_363, ), )
-    return (drop).forward1(_364, )
-  def forward1(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _365 = (act).forward1((fc1).forward1(argument_1, ), )
-    _366 = (fc3).forward1((drop).forward2(_365, ), )
-    return (drop).forward3(_366, )
-  def forward2(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _367 = (act).forward2((fc1).forward2(argument_1, ), )
-    _368 = (fc3).forward2((drop).forward4(_367, ), )
-    return (drop).forward5(_368, )
-  def forward3(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _369 = (act).forward3((fc1).forward3(argument_1, ), )
-    _370 = (fc3).forward3((drop).forward6(_369, ), )
-    return (drop).forward7(_370, )
-  def forward4(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _371 = (act).forward4((fc1).forward4(argument_1, ), )
-    _372 = (fc3).forward4((drop).forward8(_371, ), )
-    return (drop).forward9(_372, )
-  def forward5(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _373 = (act).forward5((fc1).forward5(argument_1, ), )
-    _374 = (fc3).forward5((drop).forward10(_373, ), )
-    return (drop).forward11(_374, )
-  def forward6(self: __torch__.NO.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _375 = (act).forward6((fc1).forward6(argument_1, ), )
-    _376 = (fc3).forward6((drop).forward12(_375, ), )
-    return (drop).forward13(_376, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO.py.debug_pkl
deleted file mode 100644
index d5efdc7851bb6d2a2a42c66464a74d13e7bd7a63..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:62ca006499e476f7e20f203454675121223301a6b1cf67d7c8283e02ddd1b13b
-size 119236
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_349.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_349.py
deleted file mode 100644
index 93427a5161038fcdbc7d78354cd03602e4b8a99d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_349.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_347.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_349.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_349.py.debug_pkl
deleted file mode 100644
index 93d80967e66c357b2d96427235efec00d2540255..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_349.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9b7903b10399b00f71db5de04af098df7a5375643943820a325ff10f503e4558
-size 89581
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_353.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_353.py
deleted file mode 100644
index 4970ec7aaec88019af2a220896ec7db6ab279429..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_353.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_352.MultiheadAttention
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_353.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_353.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_353.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_360.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_360.py
deleted file mode 100644
index 0d253a2f5198753f821e197454215830e3fb3c51..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_360.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_355.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_356.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_357.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_358.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_360.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_360.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_360.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_360.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_361.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_361.py
deleted file mode 100644
index df0b8b35129fc3b09e96344e009cd7145881f9f4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_361.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_346.LayerNorm
-  filter : __torch__.NO.___torch_mangle_349.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_350.Identity
-  attn : __torch__.NO.___torch_mangle_353.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_360.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_361.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_361.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_361.py.debug_pkl
deleted file mode 100644
index 5ce17fca55113a4cd1ce9c0163897c41863483a9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_361.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ef815db3f07ca47ebe410e1c1124c725a25c7beaafc952ca407bb759f67c27f6
-size 2912
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_365.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_365.py
deleted file mode 100644
index ac9b00c225cb8b3468dda543a4831cadf007f3d6..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_365.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_363.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_365.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_365.py.debug_pkl
deleted file mode 100644
index b555f79c6ab00a616379ddddf51b198fdc7c6c31..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_365.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:32a047742fe0cb178a565b3f5ac7df3a8a26bfa13e0c87417aba01233227e757
-size 89581
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_369.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_369.py
deleted file mode 100644
index ea354395ac35858261c9e45247bc69ea94c9213b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_369.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_368.MultiheadAttention
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_369.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_369.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_369.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_376.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_376.py
deleted file mode 100644
index 8cd56e06836952c6856c4f4a43549a8d788f46f1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_376.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_371.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_372.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_373.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_374.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_376.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_376.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_376.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_376.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_377.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_377.py
deleted file mode 100644
index 5c593e4f98e785e3696f76edb033833e9986b118..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_377.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm
-  filter : __torch__.NO.___torch_mangle_365.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_366.Identity
-  attn : __torch__.NO.___torch_mangle_369.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_370.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_376.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_377.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_377.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_377.py.debug_pkl
deleted file mode 100644
index e64d7580983c46cd0c9552e9b1c43512be402aba..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_377.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:92e050528fc5b454089a2cfa7636786b5945f97ba5bd42897b1f7597ba2cd931
-size 2912
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_381.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_381.py
deleted file mode 100644
index 94081617ebe8ce2efb23057ee3eada9555066e80..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_381.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_379.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_381.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_381.py.debug_pkl
deleted file mode 100644
index 85f9ca702bd115381166f5f5ae77e62d2ce7da06..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_381.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:40d8c00c593878390c9c1fdf701b7bf46f18580b2046b38c5699e2662c0f5a25
-size 89581
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_385.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_385.py
deleted file mode 100644
index c918d30932782cc1a86302c5e88f17395e5e5997..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_385.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_384.MultiheadAttention
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_385.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_385.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_385.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_392.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_392.py
deleted file mode 100644
index 3426c49e4cfc54889c0218f200604e9e6a7cdf84..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_392.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_387.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_389.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_390.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_391.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_392.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_392.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_392.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_392.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_393.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_393.py
deleted file mode 100644
index 2fe2a4687953b11ec99a9716f910b3f0d5856136..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_393.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_378.LayerNorm
-  filter : __torch__.NO.___torch_mangle_381.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_382.Identity
-  attn : __torch__.NO.___torch_mangle_385.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_386.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_392.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_393.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_393.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_393.py.debug_pkl
deleted file mode 100644
index 47045002b66149d37785cf3fb4a3095dceca4e20..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_393.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9690a6f9193003e83d4f5ddb09277fec9995b8d628d596f2c1685235a775831a
-size 2912
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_397.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_397.py
deleted file mode 100644
index 7aa84e6a746b76a7fba35773a087f294c5d1ce27..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_397.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_395.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_397.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_397.py.debug_pkl
deleted file mode 100644
index a2a621b81eb0bd7910bebaa53b069229bf01f0ab..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_397.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ac8c766fa643b97cc040016e43c02261a6e902b0c1096ddc9706541696598113
-size 89581
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_401.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_401.py
deleted file mode 100644
index 0c84cadde5817b0812df4b5d4f9307504dab8f97..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_401.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_400.MultiheadAttention
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_401.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_401.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_401.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_408.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_408.py
deleted file mode 100644
index 9506beecb0dc8eb1368432a2b68400347bec393e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_408.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_403.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_404.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_405.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_408.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_408.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_408.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_408.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_409.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_409.py
deleted file mode 100644
index 108724813bef0bbdae726138a3de917d9406dfbf..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_409.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_394.LayerNorm
-  filter : __torch__.NO.___torch_mangle_397.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity
-  attn : __torch__.NO.___torch_mangle_401.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_408.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_409.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_409.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_409.py.debug_pkl
deleted file mode 100644
index 59d13f39c1ff5d688e8eb4d1e406126451ddd04b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_409.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:910577252e553f244a8d30e8af20e67737e2f206810e7cfa92c37cadab9ce8df
-size 2912
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_413.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_413.py
deleted file mode 100644
index 4f40234c852a09b04df4e4f647aca080e43c76d7..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_413.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_411.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_413.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_413.py.debug_pkl
deleted file mode 100644
index 49a6c805cfcb4493d9b8b6c3c8ad8120700eed90..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_413.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9bde9fc8137778e5a703008f6847dfa3deb6544297bebbeba7507cb0d5986afc
-size 89581
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_417.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_417.py
deleted file mode 100644
index 8e065aab89d3135e5450a460a849a4659cf9d427..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_417.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_416.MultiheadAttention
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_417.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_417.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_417.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_424.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_424.py
deleted file mode 100644
index 6af7f16c1641c27928bcea46b483ba498d9d169d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_424.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_420.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_421.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_424.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_424.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_424.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_424.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_425.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_425.py
deleted file mode 100644
index e45c3f8e05f9272c3aa3815af13a9f9c1c9e1aaf..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_425.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm
-  filter : __torch__.NO.___torch_mangle_413.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity
-  attn : __torch__.NO.___torch_mangle_417.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_418.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_424.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_425.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_425.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_425.py.debug_pkl
deleted file mode 100644
index 2dbfae30f6ed6c5772eaa3772667c4f8accd5136..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_425.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:cfb649353202fa925a2b58c7f4883e354355e9290d783b2354ee0d3cc703d2c1
-size 2912
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_429.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_429.py
deleted file mode 100644
index b7c735ad9940ac3f25c60ccae0250c587b86bda2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_429.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_427.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_429.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_429.py.debug_pkl
deleted file mode 100644
index 224916686167b9cdd2801a854419147c5a4d3583..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_429.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1a6c8b79069bd3766faf46062ef266370b3b988c1cc1e14b3cb6ac75c6b13d94
-size 89581
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_433.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_433.py
deleted file mode 100644
index f4546a26b39d44e6215e6a1c45236858ff41606e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_433.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_432.MultiheadAttention
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_433.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_433.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_433.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_440.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_440.py
deleted file mode 100644
index cabcaf66e8d427da2b7950fb2a7617a27e04426e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_440.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_435.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_436.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_437.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_438.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_440.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_440.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_440.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_440.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_441.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_441.py
deleted file mode 100644
index a2da189f77e0e1617dcb1aa637a403fc569985d4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_441.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_426.LayerNorm
-  filter : __torch__.NO.___torch_mangle_429.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity
-  attn : __torch__.NO.___torch_mangle_433.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_434.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_440.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_441.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_441.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_441.py.debug_pkl
deleted file mode 100644
index 343e29f78a55c143104a0a94c284b7451b4068f1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_441.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a3ee38bb884dbb84c123ad11a60952101a7b41853ea3ea1284aa0dc06b30e809
-size 2912
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_445.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_445.py
deleted file mode 100644
index 6a9dac11b3289499072ad8ccbbf0495b9eea8e1a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_445.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_443.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_445.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_445.py.debug_pkl
deleted file mode 100644
index 8b1083ea5ce0d32731e25fc993f1752bd6c715db..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_445.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1824c2bede8e9c2f93d73841c1488866c92c8c61d55feddb95ffa46550db8415
-size 89581
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_449.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_449.py
deleted file mode 100644
index 2432a75247f8a0da5f9216e39c65b0a9cd5be3fe..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_449.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_448.MultiheadAttention
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_449.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_449.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_449.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_456.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_456.py
deleted file mode 100644
index 6843eb7c1a066c02cf3a6e938d16fa42e8bb6e45..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_456.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_451.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_452.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_453.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_454.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_456.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_456.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_456.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_456.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_457.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_457.py
deleted file mode 100644
index c55e7606dfe69502e136f7147e5d77e076ad281e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_457.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_442.LayerNorm
-  filter : __torch__.NO.___torch_mangle_445.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_446.Identity
-  attn : __torch__.NO.___torch_mangle_449.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_450.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_456.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_457.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_457.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_457.py.debug_pkl
deleted file mode 100644
index b42b76154ece12f7ddefe2c9954f533c00f86b42..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_457.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4f3d3d8b5e3b9686d5eaf39e136b91d74039ef921e196fbf031a2b661506af29
-size 2912
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_461.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_461.py
deleted file mode 100644
index 1ecafd2a33d2736efc15e5bc423fb54f8aca80a4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_461.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_459.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_461.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_461.py.debug_pkl
deleted file mode 100644
index c5de9caab8d790476c522c1c446ec595a5078d66..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_461.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6511369ad891e8d64e105e9596966d35dec9232b0604c1df3fd113fefa8a7191
-size 89581
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_465.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_465.py
deleted file mode 100644
index 51026ab5508fc30ede9740277a8936d411133e9b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_465.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_464.MultiheadAttention
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_465.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_465.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_465.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_472.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_472.py
deleted file mode 100644
index f8c29dbe815fed9f8aac611ba8dd03c5ec883f5f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_472.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_467.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_468.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_469.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_470.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_471.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_472.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_472.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_472.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_472.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_473.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_473.py
deleted file mode 100644
index 3f8c24f9b3c4411c54b346cf5be73133e0c2255c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_473.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_458.LayerNorm
-  filter : __torch__.NO.___torch_mangle_461.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_462.Identity
-  attn : __torch__.NO.___torch_mangle_465.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_466.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_472.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_473.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_473.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_473.py.debug_pkl
deleted file mode 100644
index c34ad977c05c96d6a7eec7f3030c7d8ae8a5c3f8..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_473.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:dc2eab9898607386306770ab3d1e91f2eed1a720c1b09a4250676f221acfdd8d
-size 2912
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_477.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_477.py
deleted file mode 100644
index 814cbc85a36c97da1255b60c32598539be4faad9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_477.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_475.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_477.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_477.py.debug_pkl
deleted file mode 100644
index 0dc6612dee47166e9dbdeeccf614f663ca50a2fa..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_477.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bcec7a1de82914723d9515e4d984bd0a73889cba1f24ddaa25f980ed6087af9b
-size 89581
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_481.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_481.py
deleted file mode 100644
index b7875f2381497446d1385d7a7c6f569e6a67e0e1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_481.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_480.MultiheadAttention
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_481.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_481.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_481.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_488.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_488.py
deleted file mode 100644
index 9258f7a6d0eaed128a862b0d81a8e28711e13a02..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_488.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_483.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_484.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_485.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_486.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_487.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_488.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_488.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_488.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_488.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_489.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_489.py
deleted file mode 100644
index e5e30c6bab14c47d0128ecefabfe5758949de3e3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_489.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_474.LayerNorm
-  filter : __torch__.NO.___torch_mangle_477.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_478.Identity
-  attn : __torch__.NO.___torch_mangle_481.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_482.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_488.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_489.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_489.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_489.py.debug_pkl
deleted file mode 100644
index e0c0936b4d2ed11d83de7e15520b65c9614aa77a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_489.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:474b740fa33c1bde4b803cd6d5a11a9fa5608160b35e4ada49485af02e2ba642
-size 2912
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_493.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_493.py
deleted file mode 100644
index 99b86c5edabff184ce203226f10c256c6b43bc3b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_493.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_491.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_493.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_493.py.debug_pkl
deleted file mode 100644
index 77356ac55eda5fcfce0f9b34205b200676002a50..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_493.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:410e0a15224868ccac5eb3e3c34ea4349017425ddb6236fdfabb5044ee86785b
-size 89581
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_497.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_497.py
deleted file mode 100644
index f2c0b0958e7c681905129bc68a122fd21adb7dd9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_497.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_496.MultiheadAttention
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_497.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_497.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_497.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_504.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_504.py
deleted file mode 100644
index 2ce55548d3471efd665e2fdc94091e2d9b43190e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_504.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_499.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_501.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_502.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_503.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_504.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_504.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_504.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_504.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_505.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_505.py
deleted file mode 100644
index 2f82135d17cd39c5ffedb478e96a8649283e134e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_505.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_490.LayerNorm
-  filter : __torch__.NO.___torch_mangle_493.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_494.Identity
-  attn : __torch__.NO.___torch_mangle_497.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_498.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_504.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_505.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_505.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_505.py.debug_pkl
deleted file mode 100644
index ddd8ad383598e6b8a3418cae98f68a290a5f3d75..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_505.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c4161be292c962c5c27f90ace834611f9b80c879fb165885f41554b5163dda52
-size 2912
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_509.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_509.py
deleted file mode 100644
index 422e701240a1c1c577b8210dbb8843434f408877..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_509.py
+++ /dev/null
@@ -1,389 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_507.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d
-  def forward(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 42, 90, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 384])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 768])
-    x5 = torch.fft_irfft2(x4, [42, 90], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
-  def forward1(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _29 = int(B)
-    _30 = int(B)
-    _31 = int(B)
-    _32 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _33 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _34 = int(C)
-    _35 = int(C)
-    _36 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_36)
-    _37 = torch.permute((bias).forward1(input, ), [0, 2, 1])
-    bias1 = torch.contiguous(_37)
-    x = torch.reshape(argument_1, [_32, 42, 90, _35])
-    x7 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _38 = ops.prim.NumToTensor(torch.size(x7, 1))
-    _39 = int(_38)
-    _40 = ops.prim.NumToTensor(torch.size(x7, 2))
-    x8 = torch.reshape(x7, [_31, _39, int(_40), 2, 384])
-    _41 = [torch.real(x8), torch.select(w1, 0, 0)]
-    _42 = torch.einsum("...bd, bdk->...bk", _41)
-    _43 = [torch.imag(x8), torch.select(w1, 0, 1)]
-    _44 = torch.einsum("...bd, bdk->...bk", _43)
-    input2 = torch.add(torch.sub(_42, _44), torch.select(b1, 0, 0))
-    _45 = torch.relu_(input2)
-    _46 = [torch.real(x8), torch.select(w1, 0, 1)]
-    _47 = torch.einsum("...bd, bdk->...bk", _46)
-    _48 = [torch.imag(x8), torch.select(w1, 0, 0)]
-    _49 = torch.einsum("...bd, bdk->...bk", _48)
-    input3 = torch.add(torch.add(_47, _49), torch.select(b1, 0, 1))
-    _50 = torch.relu_(input3)
-    _51 = torch.einsum("...bd, bdk->...bk", [_45, torch.select(w2, 0, 0)])
-    _52 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_51, _52), torch.select(b2, 0, 0))
-    _53 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _54 = torch.einsum("...bd, bdk->...bk", [_50, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_53, _54), torch.select(b2, 0, 1))
-    x9 = torch.stack([x_real, x_imag], -1)
-    x10 = torch.view_as_complex(x9)
-    _55 = ops.prim.NumToTensor(torch.size(x10, 1))
-    _56 = int(_55)
-    _57 = ops.prim.NumToTensor(torch.size(x10, 2))
-    x11 = torch.reshape(x10, [_30, _56, int(_57), 768])
-    x12 = torch.fft_irfft2(x11, [42, 90], [1, 2], "ortho")
-    x13 = torch.reshape(x12, [_29, _33, _34])
-    return torch.add(x13, bias1)
-  def forward2(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _58 = int(B)
-    _59 = int(B)
-    _60 = int(B)
-    _61 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _62 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _63 = int(C)
-    _64 = int(C)
-    _65 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_65)
-    _66 = torch.permute((bias).forward2(input, ), [0, 2, 1])
-    bias2 = torch.contiguous(_66)
-    x = torch.reshape(argument_1, [_61, 42, 90, _64])
-    x14 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _67 = ops.prim.NumToTensor(torch.size(x14, 1))
-    _68 = int(_67)
-    _69 = ops.prim.NumToTensor(torch.size(x14, 2))
-    x15 = torch.reshape(x14, [_60, _68, int(_69), 2, 384])
-    _70 = [torch.real(x15), torch.select(w1, 0, 0)]
-    _71 = torch.einsum("...bd, bdk->...bk", _70)
-    _72 = [torch.imag(x15), torch.select(w1, 0, 1)]
-    _73 = torch.einsum("...bd, bdk->...bk", _72)
-    input4 = torch.add(torch.sub(_71, _73), torch.select(b1, 0, 0))
-    _74 = torch.relu_(input4)
-    _75 = [torch.real(x15), torch.select(w1, 0, 1)]
-    _76 = torch.einsum("...bd, bdk->...bk", _75)
-    _77 = [torch.imag(x15), torch.select(w1, 0, 0)]
-    _78 = torch.einsum("...bd, bdk->...bk", _77)
-    input5 = torch.add(torch.add(_76, _78), torch.select(b1, 0, 1))
-    _79 = torch.relu_(input5)
-    _80 = torch.einsum("...bd, bdk->...bk", [_74, torch.select(w2, 0, 0)])
-    _81 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_80, _81), torch.select(b2, 0, 0))
-    _82 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _83 = torch.einsum("...bd, bdk->...bk", [_79, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_82, _83), torch.select(b2, 0, 1))
-    x16 = torch.stack([x_real, x_imag], -1)
-    x17 = torch.view_as_complex(x16)
-    _84 = ops.prim.NumToTensor(torch.size(x17, 1))
-    _85 = int(_84)
-    _86 = ops.prim.NumToTensor(torch.size(x17, 2))
-    x18 = torch.reshape(x17, [_59, _85, int(_86), 768])
-    x19 = torch.fft_irfft2(x18, [42, 90], [1, 2], "ortho")
-    x20 = torch.reshape(x19, [_58, _62, _63])
-    return torch.add(x20, bias2)
-  def forward3(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _87 = int(B)
-    _88 = int(B)
-    _89 = int(B)
-    _90 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _91 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _92 = int(C)
-    _93 = int(C)
-    _94 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_94)
-    _95 = torch.permute((bias).forward3(input, ), [0, 2, 1])
-    bias3 = torch.contiguous(_95)
-    x = torch.reshape(argument_1, [_90, 42, 90, _93])
-    x21 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _96 = ops.prim.NumToTensor(torch.size(x21, 1))
-    _97 = int(_96)
-    _98 = ops.prim.NumToTensor(torch.size(x21, 2))
-    x22 = torch.reshape(x21, [_89, _97, int(_98), 2, 384])
-    _99 = [torch.real(x22), torch.select(w1, 0, 0)]
-    _100 = torch.einsum("...bd, bdk->...bk", _99)
-    _101 = [torch.imag(x22), torch.select(w1, 0, 1)]
-    _102 = torch.einsum("...bd, bdk->...bk", _101)
-    input6 = torch.add(torch.sub(_100, _102), torch.select(b1, 0, 0))
-    _103 = torch.relu_(input6)
-    _104 = [torch.real(x22), torch.select(w1, 0, 1)]
-    _105 = torch.einsum("...bd, bdk->...bk", _104)
-    _106 = [torch.imag(x22), torch.select(w1, 0, 0)]
-    _107 = torch.einsum("...bd, bdk->...bk", _106)
-    input7 = torch.add(torch.add(_105, _107), torch.select(b1, 0, 1))
-    _108 = torch.relu_(input7)
-    _109 = torch.einsum("...bd, bdk->...bk", [_103, torch.select(w2, 0, 0)])
-    _110 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_109, _110), torch.select(b2, 0, 0))
-    _111 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _112 = torch.einsum("...bd, bdk->...bk", [_108, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_111, _112), torch.select(b2, 0, 1))
-    x23 = torch.stack([x_real, x_imag], -1)
-    x24 = torch.view_as_complex(x23)
-    _113 = ops.prim.NumToTensor(torch.size(x24, 1))
-    _114 = int(_113)
-    _115 = ops.prim.NumToTensor(torch.size(x24, 2))
-    x25 = torch.reshape(x24, [_88, _114, int(_115), 768])
-    x26 = torch.fft_irfft2(x25, [42, 90], [1, 2], "ortho")
-    x27 = torch.reshape(x26, [_87, _91, _92])
-    return torch.add(x27, bias3)
-  def forward4(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _116 = int(B)
-    _117 = int(B)
-    _118 = int(B)
-    _119 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _120 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _121 = int(C)
-    _122 = int(C)
-    _123 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_123)
-    _124 = torch.permute((bias).forward4(input, ), [0, 2, 1])
-    bias4 = torch.contiguous(_124)
-    x = torch.reshape(argument_1, [_119, 42, 90, _122])
-    x28 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _125 = ops.prim.NumToTensor(torch.size(x28, 1))
-    _126 = int(_125)
-    _127 = ops.prim.NumToTensor(torch.size(x28, 2))
-    x29 = torch.reshape(x28, [_118, _126, int(_127), 2, 384])
-    _128 = [torch.real(x29), torch.select(w1, 0, 0)]
-    _129 = torch.einsum("...bd, bdk->...bk", _128)
-    _130 = [torch.imag(x29), torch.select(w1, 0, 1)]
-    _131 = torch.einsum("...bd, bdk->...bk", _130)
-    input8 = torch.add(torch.sub(_129, _131), torch.select(b1, 0, 0))
-    _132 = torch.relu_(input8)
-    _133 = [torch.real(x29), torch.select(w1, 0, 1)]
-    _134 = torch.einsum("...bd, bdk->...bk", _133)
-    _135 = [torch.imag(x29), torch.select(w1, 0, 0)]
-    _136 = torch.einsum("...bd, bdk->...bk", _135)
-    input9 = torch.add(torch.add(_134, _136), torch.select(b1, 0, 1))
-    _137 = torch.relu_(input9)
-    _138 = torch.einsum("...bd, bdk->...bk", [_132, torch.select(w2, 0, 0)])
-    _139 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_138, _139), torch.select(b2, 0, 0))
-    _140 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _141 = torch.einsum("...bd, bdk->...bk", [_137, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_140, _141), torch.select(b2, 0, 1))
-    x30 = torch.stack([x_real, x_imag], -1)
-    x31 = torch.view_as_complex(x30)
-    _142 = ops.prim.NumToTensor(torch.size(x31, 1))
-    _143 = int(_142)
-    _144 = ops.prim.NumToTensor(torch.size(x31, 2))
-    x32 = torch.reshape(x31, [_117, _143, int(_144), 768])
-    x33 = torch.fft_irfft2(x32, [42, 90], [1, 2], "ortho")
-    x34 = torch.reshape(x33, [_116, _120, _121])
-    return torch.add(x34, bias4)
-  def forward5(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _145 = int(B)
-    _146 = int(B)
-    _147 = int(B)
-    _148 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _149 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _150 = int(C)
-    _151 = int(C)
-    _152 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_152)
-    _153 = torch.permute((bias).forward5(input, ), [0, 2, 1])
-    bias5 = torch.contiguous(_153)
-    x = torch.reshape(argument_1, [_148, 42, 90, _151])
-    x35 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _154 = ops.prim.NumToTensor(torch.size(x35, 1))
-    _155 = int(_154)
-    _156 = ops.prim.NumToTensor(torch.size(x35, 2))
-    x36 = torch.reshape(x35, [_147, _155, int(_156), 2, 384])
-    _157 = [torch.real(x36), torch.select(w1, 0, 0)]
-    _158 = torch.einsum("...bd, bdk->...bk", _157)
-    _159 = [torch.imag(x36), torch.select(w1, 0, 1)]
-    _160 = torch.einsum("...bd, bdk->...bk", _159)
-    input10 = torch.add(torch.sub(_158, _160), torch.select(b1, 0, 0))
-    _161 = torch.relu_(input10)
-    _162 = [torch.real(x36), torch.select(w1, 0, 1)]
-    _163 = torch.einsum("...bd, bdk->...bk", _162)
-    _164 = [torch.imag(x36), torch.select(w1, 0, 0)]
-    _165 = torch.einsum("...bd, bdk->...bk", _164)
-    input11 = torch.add(torch.add(_163, _165), torch.select(b1, 0, 1))
-    _166 = torch.relu_(input11)
-    _167 = torch.einsum("...bd, bdk->...bk", [_161, torch.select(w2, 0, 0)])
-    _168 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_167, _168), torch.select(b2, 0, 0))
-    _169 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _170 = torch.einsum("...bd, bdk->...bk", [_166, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_169, _170), torch.select(b2, 0, 1))
-    x37 = torch.stack([x_real, x_imag], -1)
-    x38 = torch.view_as_complex(x37)
-    _171 = ops.prim.NumToTensor(torch.size(x38, 1))
-    _172 = int(_171)
-    _173 = ops.prim.NumToTensor(torch.size(x38, 2))
-    x39 = torch.reshape(x38, [_146, _172, int(_173), 768])
-    x40 = torch.fft_irfft2(x39, [42, 90], [1, 2], "ortho")
-    x41 = torch.reshape(x40, [_145, _149, _150])
-    return torch.add(x41, bias5)
-  def forward6(self: __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _174 = int(B)
-    _175 = int(B)
-    _176 = int(B)
-    _177 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _178 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _179 = int(C)
-    _180 = int(C)
-    _181 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_181)
-    _182 = torch.permute((bias).forward6(input, ), [0, 2, 1])
-    bias6 = torch.contiguous(_182)
-    x = torch.reshape(argument_1, [_177, 42, 90, _180])
-    x42 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _183 = ops.prim.NumToTensor(torch.size(x42, 1))
-    _184 = int(_183)
-    _185 = ops.prim.NumToTensor(torch.size(x42, 2))
-    x43 = torch.reshape(x42, [_176, _184, int(_185), 2, 384])
-    _186 = [torch.real(x43), torch.select(w1, 0, 0)]
-    _187 = torch.einsum("...bd, bdk->...bk", _186)
-    _188 = [torch.imag(x43), torch.select(w1, 0, 1)]
-    _189 = torch.einsum("...bd, bdk->...bk", _188)
-    input12 = torch.add(torch.sub(_187, _189), torch.select(b1, 0, 0))
-    _190 = torch.relu_(input12)
-    _191 = [torch.real(x43), torch.select(w1, 0, 1)]
-    _192 = torch.einsum("...bd, bdk->...bk", _191)
-    _193 = [torch.imag(x43), torch.select(w1, 0, 0)]
-    _194 = torch.einsum("...bd, bdk->...bk", _193)
-    input13 = torch.add(torch.add(_192, _194), torch.select(b1, 0, 1))
-    _195 = torch.relu_(input13)
-    _196 = torch.einsum("...bd, bdk->...bk", [_190, torch.select(w2, 0, 0)])
-    _197 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_196, _197), torch.select(b2, 0, 0))
-    _198 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _199 = torch.einsum("...bd, bdk->...bk", [_195, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_198, _199), torch.select(b2, 0, 1))
-    x44 = torch.stack([x_real, x_imag], -1)
-    x45 = torch.view_as_complex(x44)
-    _200 = ops.prim.NumToTensor(torch.size(x45, 1))
-    _201 = int(_200)
-    _202 = ops.prim.NumToTensor(torch.size(x45, 2))
-    x46 = torch.reshape(x45, [_175, _201, int(_202), 768])
-    x47 = torch.fft_irfft2(x46, [42, 90], [1, 2], "ortho")
-    x48 = torch.reshape(x47, [_174, _178, _179])
-    return torch.add(x48, bias6)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_509.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_509.py.debug_pkl
deleted file mode 100644
index 898db8f9922a48487a8678875515ee93a0a52952..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_509.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e082564c44ea855cdb91d82bdc7f72c008ba83c136b03cc321a4041b62624df7
-size 89581
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_513.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_513.py
deleted file mode 100644
index e343f5408feb0e565cfc9d80fadd35d75a450aae..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_513.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MultiHeadSelfAttention(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  multihead_attn : __torch__.torch.nn.modules.activation.___torch_mangle_512.MultiheadAttention
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_513.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_513.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_513.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_520.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_520.py
deleted file mode 100644
index c0e9f3b6e0810ddce1199b2f8e2b78177a7da1e5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_520.py
+++ /dev/null
@@ -1,73 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_515.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_517.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_518.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_519.Dropout
-  def forward(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
-  def forward1(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _2 = (act).forward1((fc1).forward1(argument_1, ), )
-    _3 = (fc3).forward1((drop).forward2(_2, ), )
-    return (drop).forward3(_3, )
-  def forward2(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _4 = (act).forward2((fc1).forward2(argument_1, ), )
-    _5 = (fc3).forward2((drop).forward4(_4, ), )
-    return (drop).forward5(_5, )
-  def forward3(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _6 = (act).forward3((fc1).forward3(argument_1, ), )
-    _7 = (fc3).forward3((drop).forward6(_6, ), )
-    return (drop).forward7(_7, )
-  def forward4(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _8 = (act).forward4((fc1).forward4(argument_1, ), )
-    _9 = (fc3).forward4((drop).forward8(_8, ), )
-    return (drop).forward9(_9, )
-  def forward5(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _10 = (act).forward5((fc1).forward5(argument_1, ), )
-    _11 = (fc3).forward5((drop).forward10(_10, ), )
-    return (drop).forward11(_11, )
-  def forward6(self: __torch__.NO.___torch_mangle_520.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _12 = (act).forward6((fc1).forward6(argument_1, ), )
-    _13 = (fc3).forward6((drop).forward12(_12, ), )
-    return (drop).forward13(_13, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_520.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_520.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_520.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_521.py b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_521.py
deleted file mode 100644
index dc7c9b758b44e1b522d788691f24c9bc49a3968b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_521.py
+++ /dev/null
@@ -1,102 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_506.LayerNorm
-  filter : __torch__.NO.___torch_mangle_509.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_510.Identity
-  attn : __torch__.NO.___torch_mangle_513.MultiHeadSelfAttention
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_514.LayerNorm
-  mlp : __torch__.NO.___torch_mangle_520.Mlp
-  def forward(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
-  def forward1(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _4 = (filter).forward1((normlayer1).forward1(argument_1, ), )
-    _5 = (drop_path).forward2()
-    input = torch.add(argument_1, _4)
-    _6 = (mlp).forward1((normlayer2).forward1(input, ), )
-    _7 = (drop_path).forward3()
-    return torch.add(input, _6)
-  def forward2(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _8 = (filter).forward2((normlayer1).forward2(argument_1, ), )
-    _9 = (drop_path).forward4()
-    input = torch.add(argument_1, _8)
-    _10 = (mlp).forward2((normlayer2).forward2(input, ), )
-    _11 = (drop_path).forward5()
-    return torch.add(input, _10)
-  def forward3(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _12 = (filter).forward3((normlayer1).forward3(argument_1, ), )
-    _13 = (drop_path).forward6()
-    input = torch.add(argument_1, _12)
-    _14 = (mlp).forward3((normlayer2).forward3(input, ), )
-    _15 = (drop_path).forward7()
-    return torch.add(input, _14)
-  def forward4(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _16 = (filter).forward4((normlayer1).forward4(argument_1, ), )
-    _17 = (drop_path).forward8()
-    input = torch.add(argument_1, _16)
-    _18 = (mlp).forward4((normlayer2).forward4(input, ), )
-    _19 = (drop_path).forward9()
-    return torch.add(input, _18)
-  def forward5(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _20 = (filter).forward5((normlayer1).forward5(argument_1, ), )
-    _21 = (drop_path).forward10()
-    input = torch.add(argument_1, _20)
-    _22 = (mlp).forward5((normlayer2).forward5(input, ), )
-    _23 = (drop_path).forward11()
-    return torch.add(input, _22)
-  def forward6(self: __torch__.NO.___torch_mangle_521.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _24 = (filter).forward6((normlayer1).forward6(argument_1, ), )
-    _25 = (drop_path).forward12()
-    input = torch.add(argument_1, _24)
-    _26 = (mlp).forward6((normlayer2).forward6(input, ), )
-    _27 = (drop_path).forward13()
-    return torch.add(input, _26)
diff --git a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_521.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_521.py.debug_pkl
deleted file mode 100644
index d384ea1fbc8f3db4e55515970e216e1864967ae1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/NO/___torch_mangle_521.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fa0aa918d173ce1584da0e4e683570f32e7adc9c363359c4a7ffb2c02e62128f
-size 2912
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock.py b/xe_weights14/glonet_p3/code/__torch__/dblock.py
deleted file mode 100644
index 17b7bcea8e379b9efe2204d2834439fa35070bcb..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock.py
+++ /dev/null
@@ -1,160 +0,0 @@
-class TeDev(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_541.LayerNorm
-  enc : __torch__.torch.nn.modules.container.___torch_mangle_692.Sequential
-  blocks : __torch__.torch.nn.modules.container.___torch_mangle_846.ModuleList
-  dec : __torch__.torch.nn.modules.container.___torch_mangle_999.Sequential
-  def forward(self: __torch__.dblock.TeDev,
-    x: Tensor) -> Tensor:
-    dec = self.dec
-    _7 = getattr(dec, "7")
-    dec0 = self.dec
-    _6 = getattr(dec0, "6")
-    dec1 = self.dec
-    _5 = getattr(dec1, "5")
-    dec2 = self.dec
-    _4 = getattr(dec2, "4")
-    dec3 = self.dec
-    _3 = getattr(dec3, "3")
-    dec4 = self.dec
-    _2 = getattr(dec4, "2")
-    dec5 = self.dec
-    _1 = getattr(dec5, "1")
-    dec6 = self.dec
-    _0 = getattr(dec6, "0")
-    norm = self.norm
-    blocks = self.blocks
-    _11 = getattr(blocks, "11")
-    blocks0 = self.blocks
-    _10 = getattr(blocks0, "10")
-    blocks1 = self.blocks
-    _9 = getattr(blocks1, "9")
-    blocks2 = self.blocks
-    _8 = getattr(blocks2, "8")
-    blocks3 = self.blocks
-    _70 = getattr(blocks3, "7")
-    blocks4 = self.blocks
-    _60 = getattr(blocks4, "6")
-    blocks5 = self.blocks
-    _50 = getattr(blocks5, "5")
-    blocks6 = self.blocks
-    _40 = getattr(blocks6, "4")
-    blocks7 = self.blocks
-    _30 = getattr(blocks7, "3")
-    blocks8 = self.blocks
-    _20 = getattr(blocks8, "2")
-    blocks9 = self.blocks
-    _12 = getattr(blocks9, "1")
-    blocks10 = self.blocks
-    _00 = getattr(blocks10, "0")
-    enc = self.enc
-    _71 = getattr(enc, "7")
-    enc0 = self.enc
-    _61 = getattr(enc0, "6")
-    enc1 = self.enc
-    _51 = getattr(enc1, "5")
-    enc2 = self.enc
-    _41 = getattr(enc2, "4")
-    enc3 = self.enc
-    _31 = getattr(enc3, "3")
-    enc4 = self.enc
-    _21 = getattr(enc4, "2")
-    enc5 = self.enc
-    _13 = getattr(enc5, "1")
-    enc6 = self.enc
-    _01 = getattr(enc6, "0")
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _14 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _15 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _16 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _17 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _18 = int(W)
-    _19 = [_14, int(torch.mul(T, C)), _17, _18]
-    input = torch.reshape(x, _19)
-    _22 = (_01).forward(input, )
-    _23 = (_13).forward(_22, )
-    _24 = (_21).forward(_23, )
-    _25 = (_31).forward(_24, )
-    _26 = (_41).forward(_25, )
-    _27 = (_51).forward(_26, )
-    _28 = (_61).forward(_27, )
-    _29 = (_71).forward(_28, )
-    B0 = ops.prim.NumToTensor(torch.size(_29, 0))
-    _32 = int(B0)
-    _33 = int(B0)
-    _34 = int(B0)
-    D = ops.prim.NumToTensor(torch.size(_29, 1))
-    _35 = int(D)
-    _36 = int(D)
-    H0 = ops.prim.NumToTensor(torch.size(_29, 2))
-    _37 = int(H0)
-    _38 = int(H0)
-    W0 = ops.prim.NumToTensor(torch.size(_29, 3))
-    _39 = int(W0)
-    _42 = int(W0)
-    N = torch.mul(H0, W0)
-    _43 = int(N)
-    z = torch.contiguous(torch.permute(_29, [0, 2, 3, 1]))
-    input0 = torch.view(z, [_34, _43, _36])
-    _44 = (_12).forward((_00).forward(input0, ), )
-    _45 = (_30).forward((_20).forward(_44, ), )
-    _46 = (_50).forward((_40).forward(_45, ), )
-    _47 = (_70).forward((_60).forward(_46, ), )
-    _48 = (_10).forward((_9).forward((_8).forward(_47, ), ), )
-    _49 = (norm).forward((_11).forward(_48, ), )
-    z0 = torch.contiguous(torch.permute(_49, [0, 2, 1]))
-    input1 = torch.reshape(z0, [_33, _35, _38, _42])
-    input2 = torch.cat([(_0).forward(input1, ), _28], 1)
-    input3 = torch.cat([(_1).forward(input2, ), _27], 1)
-    input4 = torch.cat([(_2).forward(input3, ), _26], 1)
-    input5 = torch.cat([(_3).forward(input4, ), _25], 1)
-    input6 = torch.cat([(_4).forward(input5, ), _24], 1)
-    input7 = torch.cat([(_5).forward(input6, ), _23], 1)
-    input8 = torch.cat([(_6).forward(input7, ), _22], 1)
-    y = torch.reshape((_7).forward(input8, ), [_32, _15, _16, _37, _39])
-    return torch.add(y, x)
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_542.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_558.Sequential
-  def forward(self: __torch__.dblock.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _30 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_30, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_30, ))
-    y1 = torch.add_(y0, (_2).forward(_30, ))
-    return torch.add_(y1, (_3).forward(_30, ))
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_543.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_544.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_545.LeakyReLU
-  def forward(self: __torch__.dblock.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _31 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_31, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock.py.debug_pkl
deleted file mode 100644
index c13f4e6e5708a9c6bbeeaa05b3136754433c12b4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_549.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_549.py
deleted file mode 100644
index 0926398c6f28a7475e4ff8016ca5554166e16639..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_549.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_546.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_547.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_548.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_549.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_549.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_549.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_549.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_553.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_553.py
deleted file mode 100644
index 8f9ba243b10b7efaba9b158d3076528e14ffc303..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_553.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_550.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_551.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_552.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_553.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_553.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_553.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_553.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_557.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_557.py
deleted file mode 100644
index c9638ad03090a62cdb9b76a1df7ed0e56135d11a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_557.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_554.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_555.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_556.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_557.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_557.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_557.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_557.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_563.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_563.py
deleted file mode 100644
index 14f10363b3ff5ffed26868ab0af139bef8af4f0a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_563.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_560.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_561.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_562.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_563.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_563.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_563.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_563.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_567.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_567.py
deleted file mode 100644
index 5a87dd1c12757d9cb1da748ec7e6ff22788d18d4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_567.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_564.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_565.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_566.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_567.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_567.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_567.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_567.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_571.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_571.py
deleted file mode 100644
index f1a8c968331a80cc08230816c10cd3476f1429d2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_571.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_568.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_569.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_570.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_571.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_571.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_571.py.debug_pkl
deleted file mode 100644
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+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_572.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_573.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_574.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_575.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_575.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_575.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_575.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_577.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_577.py
deleted file mode 100644
index b308374844388faba3c28fa7b6e7b4925de74ed4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_577.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_559.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_576.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_577.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_577.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_577.py.debug_pkl
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index 04d3899e14955fe3d766ed1095b2140ea5870c3c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_577.py.debug_pkl
+++ /dev/null
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_582.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_582.py
deleted file mode 100644
index 98d4879266bdf52adfb68ed65c7f9dfd5ffc4958..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_582.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_579.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_580.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_581.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_582.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_582.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_582.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_582.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_586.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_586.py
deleted file mode 100644
index 0af0c27e64b64f1b31bfbb5de58fadd6f743cd91..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_586.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_583.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_584.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_585.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_586.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_586.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_586.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_586.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
index c7637fe96d2b6f5dad03bc54aa9fc27197ad58ac..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_590.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_587.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_588.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_589.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_590.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_590.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_590.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_590.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_594.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_594.py
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_594.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_591.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_592.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_593.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_594.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_594.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_594.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_594.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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index be847eae65500962129005fc29e9a749f8038111..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_596.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_578.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_595.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_596.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_596.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_596.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_596.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_601.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_601.py
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_601.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_598.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_599.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_600.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_601.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_601.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_601.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_601.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_605.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_605.py
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_605.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_602.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_603.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_604.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_605.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_605.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_605.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_605.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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index 4cdf90b966029c5067d2736b7e7389462fe3890c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_609.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_606.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_607.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_608.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_609.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
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-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_609.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_609.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_613.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_610.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_611.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_612.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_613.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_613.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_613.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_615.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_615.py
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_615.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_597.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_614.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_615.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
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-    lls1 = self.lls
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-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_615.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_615.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_615.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_620.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_620.py
deleted file mode 100644
index e14d4070a7bca2f8fbf5666f13495ff02a6c1f88..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_620.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_617.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_618.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_619.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_620.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_620.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_620.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_620.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
index 9e49610ed43045ec891f655110ee2494fdd7426e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_624.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_621.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_622.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_623.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_624.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_624.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_624.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_624.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_628.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_628.py
deleted file mode 100644
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_628.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_625.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_626.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_627.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_628.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_628.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_628.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_628.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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index 69711701390c673909820d5ff83cb51a1de24140..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_632.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_629.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_630.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_631.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_632.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_632.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_632.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_632.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_634.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_634.py
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index 1162eaaef8238f3ed997e6942deae234ef53c063..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_634.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_616.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_633.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_634.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_634.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_634.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_634.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_639.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_639.py
deleted file mode 100644
index 1ce535f9f30e0f46435dd872feb1f36da5228a86..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_639.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_636.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_637.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_638.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_639.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_639.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_639.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_639.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_643.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_643.py
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index b2994eed737a9b32177cf6e9bc2f45065e80f42a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_643.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_640.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_641.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_642.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_643.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_643.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_643.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_643.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_647.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_644.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_645.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_646.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_647.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_647.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_647.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_647.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_651.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_651.py
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_651.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_648.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_649.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_650.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_651.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_651.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_651.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_651.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_653.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_653.py
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_653.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_635.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_652.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_653.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
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-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_653.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_653.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_653.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_658.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_658.py
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_658.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_655.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_656.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_657.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_658.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_658.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_658.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_658.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_662.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_662.py
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_662.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_659.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_660.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_661.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_662.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_662.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_662.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_662.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_666.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_666.py
deleted file mode 100644
index c6c5e4489bf00282ce6c68e207aade2d6866cdb7..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_666.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_663.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_664.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_665.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_666.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_666.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_666.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_666.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_670.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_670.py
deleted file mode 100644
index cf69c8ac9e04a78d8de1b3053a9587ea5a7d98c0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_670.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_667.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_668.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_669.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_670.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_670.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_670.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_670.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_672.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_672.py
deleted file mode 100644
index 0d15292de2dc1f5d3f1cb813f78f1c51532cc521..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_672.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_654.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_671.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_672.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_672.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_672.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_672.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_677.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_677.py
deleted file mode 100644
index adfd79947e903a2d5689253faf6bc1726d460677..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_677.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_674.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_675.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_676.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_677.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_677.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_677.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_677.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_681.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_681.py
deleted file mode 100644
index ea0bc5806f156b04e1cfeca3819c126b3876011e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_681.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_678.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_679.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_680.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_681.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_681.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_681.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_681.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_685.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_685.py
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index 9ff137a038baf2fc57cbbae0c5bcaf992001b2af..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_685.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_682.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_683.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_684.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_685.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_685.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_685.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_685.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_689.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_689.py
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index a4704ed24c701f9b4a3d088438eaf10b819b8934..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_689.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_686.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_687.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_688.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_689.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_689.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_689.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_689.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_691.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_691.py
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index 4072e9b6786707376a3559efccad9e83727d0d86..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_691.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_673.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_690.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_691.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
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-    lls2 = self.lls
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-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_691.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_691.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_691.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_851.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_851.py
deleted file mode 100644
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_851.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_848.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_849.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_850.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_851.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_851.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_851.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_851.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_855.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_855.py
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_855.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_852.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_853.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_854.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_855.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_855.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_855.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_855.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_859.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_856.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_857.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_858.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_859.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_859.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_859.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_863.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_863.py
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_863.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_860.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_861.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_862.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_863.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_863.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_863.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_863.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_865.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_865.py
deleted file mode 100644
index b2a1efb57079c24bedbd1736fa84ede97fb8c878..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_865.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_847.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_864.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_865.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_865.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_865.py.debug_pkl
deleted file mode 100644
index 45f5ec4108337247673d196246c14a623cb82e26..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_865.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_870.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_870.py
deleted file mode 100644
index 503c7de5f5c95004e7cb113a89f3264df66c2295..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_870.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_867.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_868.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_869.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_870.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_870.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_870.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_870.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_874.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_874.py
deleted file mode 100644
index 6a0ace4cec97e9d53cdb5c1941ffcec619fc0d19..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_874.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_871.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_872.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_873.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_874.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_874.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_874.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_874.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_878.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_878.py
deleted file mode 100644
index 094d6182b83d1a0943059feb5d80ed188908c6c4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_878.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_875.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_876.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_877.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_878.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_878.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_878.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_878.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_882.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_882.py
deleted file mode 100644
index 4e29a152a94094bcffc007208b1ce2ca9528c08e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_882.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_879.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_880.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_881.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_882.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_882.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_882.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_882.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_884.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_884.py
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index 8f38d0d51b5d8bb9d7f2651394e8d7d7501538dd..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_884.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_866.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_883.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_884.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_884.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_884.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_884.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_889.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_889.py
deleted file mode 100644
index 538e0ffe9bd70126a1ef16b86ed8a7c36bfd9425..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_889.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_886.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_887.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_888.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_889.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_889.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_889.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_889.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_893.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_893.py
deleted file mode 100644
index 22ff9a3c5602b0cc8e17b636ec73b6a8000146cc..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_893.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_890.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_891.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_892.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_893.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_893.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_893.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_893.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_897.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_897.py
deleted file mode 100644
index b2f4f65dfe1133a30d581855d308bb7ec39ec279..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_897.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_894.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_895.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_896.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_897.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_897.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_897.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_897.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_901.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_901.py
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index ac540d7f8ec486e406af35314e033e6889aadc2b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_901.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_898.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_899.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_900.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_901.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_901.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_901.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_903.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_903.py
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_903.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_885.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_902.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_903.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_903.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_903.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_903.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_908.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_908.py
deleted file mode 100644
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_908.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_905.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_906.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_907.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_908.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_908.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_908.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_908.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_912.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_912.py
deleted file mode 100644
index b994569ff3a60346240199dbbc625411b97a6937..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_912.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_909.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_910.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_911.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_912.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_912.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_912.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_912.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_916.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_913.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_914.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_915.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_916.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_916.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_916.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_916.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_920.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_917.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_918.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_919.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_920.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_920.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_920.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_922.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_904.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_921.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_922.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
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-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_922.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_922.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_922.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_927.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_927.py
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_927.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_924.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_925.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_926.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_927.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_927.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_927.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_927.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_931.py
+++ /dev/null
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-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_928.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_929.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_930.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_931.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
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-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_931.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_931.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_931.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_935.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_932.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_933.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_934.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_935.GroupConv2d,
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-    activate = self.activate
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-    conv = self.conv
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-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_935.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_935.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_939.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_936.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_937.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_938.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_939.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_939.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_939.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_941.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_941.py
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_941.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_923.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_940.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_941.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
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-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_941.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_941.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_941.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_946.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_946.py
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_946.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_943.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_944.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_945.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_946.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_946.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_946.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_950.py
+++ /dev/null
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-class GroupConv2d(Module):
-  __parameters__ = []
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_947.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_948.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_949.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_950.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_950.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_950.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_950.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_954.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_954.py
deleted file mode 100644
index 926d6373f54594737fbc869ad8a1759368842c1a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_954.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_951.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_952.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_953.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_954.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_954.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_954.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_954.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_958.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_958.py
deleted file mode 100644
index f6dd39949ec197ab8a4b72cbec202a23641905c4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_958.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_955.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_956.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_957.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_958.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_958.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_958.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_958.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_960.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_960.py
deleted file mode 100644
index 8cd6b8431d708b47e81f02f4681407486279d15c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_960.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_942.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_959.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_960.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_960.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_960.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_960.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_965.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_965.py
deleted file mode 100644
index 01ab2f360a3cc4081bf30860f6e01572db8c0011..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_965.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_962.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_963.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_964.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_965.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_965.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_965.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_965.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_969.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_966.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_967.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_968.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_969.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_969.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_969.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_969.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_973.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_970.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_971.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_972.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_973.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_973.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_973.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_973.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_977.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_977.py
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_977.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_974.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_975.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_976.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_977.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_977.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_977.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_979.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_979.py
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_979.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_961.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_978.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_979.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
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-    lls1 = self.lls
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-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_979.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_979.py.debug_pkl
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+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_984.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_984.py
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_984.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_981.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_982.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_983.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_984.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_984.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_984.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_984.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_988.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_985.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_986.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_987.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_988.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_988.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_988.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_992.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_989.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_990.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_991.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_992.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_992.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_992.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_996.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_996.py
deleted file mode 100644
index 0fc7b031dfb8890175beff5b40570a464b2d5d2a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_996.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_993.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_994.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_995.LeakyReLU
-  def forward(self: __torch__.dblock.___torch_mangle_996.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_996.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_996.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_996.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_998.py b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_998.py
deleted file mode 100644
index cba35dd15f504e961e33b5462b147e234737241f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_998.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_980.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_997.Sequential
-  def forward(self: __torch__.dblock.___torch_mangle_998.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_998.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_998.py.debug_pkl
deleted file mode 100644
index 7b5b29081ded0d8b43764c35640b2bcd14116720..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/dblock/___torch_mangle_998.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:588ca30c94a49501c9c7a2aa4e2c0ef7a2293d792e7b2bfa3515226156c264f2
-size 1771
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu.py b/xe_weights14/glonet_p3/code/__torch__/fcu.py
deleted file mode 100644
index b73075e7f2aaedb75cc7daec2932a8bdeee0c363..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu.py
+++ /dev/null
@@ -1,107 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_693.LayerNorm
-  filter : __torch__.fcu.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_696.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_697.LayerNorm
-  mlp : __torch__.fcu.Mlp
-  def forward(self: __torch__.fcu.FourierNetBlock,
-    input: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(input, ), )
-    _1 = (drop_path).forward()
-    input0 = torch.add(input, _0)
-    _2 = (mlp).forward((normlayer2).forward(input0, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input0, _2)
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_694.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_695.Conv1d
-  def forward(self: __torch__.fcu.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _4 = int(B)
-    _5 = int(B)
-    _6 = int(B)
-    _7 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _8 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _9 = int(C)
-    _10 = int(C)
-    _11 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_11)
-    _12 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_12)
-    x = torch.reshape(argument_1, [_7, 336, 720, _10])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _13 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _14 = int(_13)
-    _15 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_6, _14, int(_15), 2, 64])
-    _16 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _17 = torch.einsum("...bd, bdk->...bk", _16)
-    _18 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _19 = torch.einsum("...bd, bdk->...bk", _18)
-    input1 = torch.add(torch.sub(_17, _19), torch.select(b1, 0, 0))
-    _20 = torch.relu_(input1)
-    _21 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _22 = torch.einsum("...bd, bdk->...bk", _21)
-    _23 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _24 = torch.einsum("...bd, bdk->...bk", _23)
-    input2 = torch.add(torch.add(_22, _24), torch.select(b1, 0, 1))
-    _25 = torch.relu_(input2)
-    _26 = torch.einsum("...bd, bdk->...bk", [_20, torch.select(w2, 0, 0)])
-    _27 = torch.einsum("...bd, bdk->...bk", [_25, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_26, _27), torch.select(b2, 0, 0))
-    _28 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _29 = torch.einsum("...bd, bdk->...bk", [_25, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_28, _29), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _30 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _31 = int(_30)
-    _32 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_5, _31, int(_32), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_4, _8, _9])
-    return torch.add(x6, bias0)
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_698.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_699.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_700.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_701.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_702.Dropout
-  def forward(self: __torch__.fcu.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _33 = (act).forward((fc1).forward(argument_1, ), )
-    _34 = (fc3).forward((drop).forward(_33, ), )
-    return (drop).forward1(_34, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu.py.debug_pkl
deleted file mode 100644
index 8be1c52844323153f3ebda49f2f55f42dc84e060..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ea50cd17162efae2f9babaa4254e57108401dd52b2af3306f6db635241c11bd3
-size 13848
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_706.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_706.py
deleted file mode 100644
index 24f11bfb5025bebe77c9e77e87c687f285e6e103..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_706.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_704.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_705.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_706.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_706.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_706.py.debug_pkl
deleted file mode 100644
index 0f6c91f9e535269b818b66d3317d4756e4a35fdd..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_706.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fb918554cec6c172a15b4290d139a97f0516874894634b392567eed94b5dc787
-size 13614
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_714.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_714.py
deleted file mode 100644
index 2cd22cbfc39c1b63f28d2a86a80d962c0b657d43..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_714.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_709.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_710.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_711.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_712.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_713.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_714.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_714.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_714.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_714.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_715.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_715.py
deleted file mode 100644
index 8e46abe883d9c4e5bcc080705ae394534ba35056..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_715.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_703.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_706.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_707.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_708.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_714.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_715.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_715.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_715.py.debug_pkl
deleted file mode 100644
index 5a6ca96dcddb3156a44ac16a11f76ae89c10b506..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_715.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2f8ac4611cd57057b7b91d38295c1e932a8588ad79da691a48b878858fb359f8
-size 1496
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_719.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_719.py
deleted file mode 100644
index 3b9fc1085a22df614d1a22642d8177685bab0c0b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_719.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_717.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_718.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_719.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_719.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_719.py.debug_pkl
deleted file mode 100644
index 4ca7223cc93ebe21304bb1a2cd81da2f01484b1c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_719.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:82285c58a1e0020a3ef17042c7dd0f7601a9a48603ecbec8493ec86d047dde36
-size 13614
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_727.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_727.py
deleted file mode 100644
index aa5499a7f950cc4c15758302f29e731d7358ab09..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_727.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_722.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_723.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_724.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_725.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_726.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_727.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_727.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_727.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_727.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_728.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_728.py
deleted file mode 100644
index 142cc83f4021c365891644552c9178b438051778..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_728.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_716.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_719.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_720.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_721.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_727.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_728.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_728.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_728.py.debug_pkl
deleted file mode 100644
index 9003c8d5ab0a59b960ed55518660550cb1a0958f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_728.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:85360649f5e60f6f6dfb014cd297ea5550a9c04f4c59758b9939ceb9e21171de
-size 1496
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_732.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_732.py
deleted file mode 100644
index 31003d6d7276b43cc7a672c7fce5cfd88bcdb966..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_732.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_730.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_731.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_732.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_732.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_732.py.debug_pkl
deleted file mode 100644
index e3fa17b1eee03a588d6c8cd124263c46c3d64b8d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_732.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c148723d5627b481817acd6a40967984c182fd6f4b21d63e6f0936a5ce9bbeaa
-size 13614
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_740.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_740.py
deleted file mode 100644
index 930a786a4bc0008ff13ca06df553fd401494f368..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_740.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_735.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_736.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_737.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_738.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_739.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_740.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_740.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_740.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_740.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_741.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_741.py
deleted file mode 100644
index bc5fb45af290e57386e00835a605f429a79c75d1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_741.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_729.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_732.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_733.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_734.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_740.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_741.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_741.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_741.py.debug_pkl
deleted file mode 100644
index 38690a5515305804224d869d88c15e7c288970c7..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_741.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:420af5518ed7b84bed78d1a91d2c4f308d7edd61160841839e5b516f2e9036b5
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diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_745.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_745.py
deleted file mode 100644
index cd9b935344c492e54e654e80afffc77a93114a1c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_745.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_743.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_744.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_745.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_745.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_745.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_745.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c635ca0ead1ed5b017b4ec8512a7e0e210af01bd712d469656f66b58d1ee1f26
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diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_753.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_753.py
deleted file mode 100644
index bb0c094493892bc72f64fe4fd39ca728beb9edc7..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_753.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_748.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_749.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_750.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_751.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_752.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_753.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_753.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_753.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_753.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_754.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_754.py
deleted file mode 100644
index 7a2d10f6f720b0e6b3a0e9b7ecf6b00863b05f22..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_754.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_742.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_745.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_746.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_747.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_753.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_754.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_754.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_754.py.debug_pkl
deleted file mode 100644
index 9b31872e8ca6d06758ca6612d9cf18b8628c7d63..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_754.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6f48ccd165fbb4295d456491eda576caa5deb4fb088dcda2b7d94ad6e3e12ed4
-size 1496
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_758.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_758.py
deleted file mode 100644
index d3d7ddda308f7572872064923a93be63162cf6bc..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_758.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_756.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_757.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_758.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_758.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_758.py.debug_pkl
deleted file mode 100644
index a3cff59ff1eade27a2c4461fc3e2c505dcbbbc6c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_758.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7f9f914053a116680ee57da9b01c2abeb26e215ca0958b688c56fd99aefa17c2
-size 13614
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_766.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_766.py
deleted file mode 100644
index a3a6ec04b5aa842b4396cb9675fdb6476e1fb835..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_766.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_761.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_762.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_763.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_764.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_765.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_766.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_766.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_766.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_766.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_767.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_767.py
deleted file mode 100644
index 2dda3658bf454e45c5ba3f96823f3af409724d72..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_767.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_755.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_758.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_759.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_760.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_766.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_767.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_767.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_767.py.debug_pkl
deleted file mode 100644
index 83a1e585052ec48742d45edeeab2ad653315ba55..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_767.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:337cef9caae8388ea6591196c854df112e485e44dc893ec8ec2a3c49d882470b
-size 1496
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_771.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_771.py
deleted file mode 100644
index 525b195fb129279799e074d74e36a2fe022c15b3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_771.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_769.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_770.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_771.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_771.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_771.py.debug_pkl
deleted file mode 100644
index 19e3dfb806e8273b2aa17311806d1ac30548ca0e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_771.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:69ba38df9a493e0827115a8e43711ac5a6966928e2dddf357d98246d48a9a9b7
-size 13614
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_779.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_779.py
deleted file mode 100644
index 5cbcb039fd1fc5d41334721dfb5a8fdde0bee381..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_779.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_774.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_775.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_776.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_777.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_778.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_779.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_779.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_779.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_779.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_780.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_780.py
deleted file mode 100644
index 3b713c8e45d2c190ca664474ab30ddfe1fabc5f8..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_780.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_768.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_771.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_772.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_773.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_779.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_780.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_780.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_780.py.debug_pkl
deleted file mode 100644
index ef5a012cf5a7186011e852051fa4555a9cfddd9f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_780.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:83a238e8b5cb2fb3fd3f8dbac95a592b8ca493e0101a23b2c76148abf156a829
-size 1496
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_784.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_784.py
deleted file mode 100644
index 590cdb028d7ac43be1a3ccd854986437fb08d521..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_784.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_782.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_783.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_784.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_784.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_784.py.debug_pkl
deleted file mode 100644
index 98b48e65a002f645eab0198e8e2d451203fd4afe..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_784.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:40c8c4f9dcad5ed420f2b3806869de6c365f2c62e4bb1cf717aa9cd7577ca184
-size 13614
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_792.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_792.py
deleted file mode 100644
index 5fbb5fe82bc52bdf3fb1ead0ff698d44340b8488..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_792.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_787.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_788.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_789.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_790.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_791.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_792.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_792.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_792.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_792.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_793.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_793.py
deleted file mode 100644
index 2382fc7122f1053c5461bc3be1dc74dbf3803e62..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_793.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_781.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_784.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_785.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_786.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_792.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_793.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_793.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_793.py.debug_pkl
deleted file mode 100644
index 5502771e47894d2a842653556b431613043bc24f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_793.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5803aeb4995f64b17be5210161e9055769873c1f839285b8f4a3f357c02318cb
-size 1496
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_797.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_797.py
deleted file mode 100644
index 9dfe5fe9ec9c3a9e42cdaa91704662dcf5030582..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_797.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_795.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_796.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_797.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_797.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_797.py.debug_pkl
deleted file mode 100644
index 11adcc1e140a01b4bdb0cf6889eaca9238270b1e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_797.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0ad7fdec090a72c28d1e0dfd7ae97dbe4cdd87f0582ab77221aac7f3fe938a0c
-size 13614
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_805.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_805.py
deleted file mode 100644
index 0ccc6e2c2d390b2eed5c153f60ff638b5e481500..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_805.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_800.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_801.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_802.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_803.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_804.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_805.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_805.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_805.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_805.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_806.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_806.py
deleted file mode 100644
index f2a50da19933288ba8096e0be7423cb19e5cb6cf..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_806.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_794.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_797.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_798.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_799.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_805.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_806.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_806.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_806.py.debug_pkl
deleted file mode 100644
index 26a214f7d7f3256aff7184a20ca0c6d5e557aae5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_806.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7aaa30f0d750803eb2ba73b615f9200356fc4783b7df5a87a1a9cb8ea9f8b00a
-size 1496
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_810.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_810.py
deleted file mode 100644
index 09cfff346f1441730b09f973d3203e979df96920..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_810.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_808.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_809.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_810.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_810.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_810.py.debug_pkl
deleted file mode 100644
index c4a84b32e0ece35cc000e891476704758336cec4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_810.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:934d9b7198713eba3b9115e68b11193cf95d5b3d35384c9cab59e53de99cc21f
-size 13614
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_818.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_818.py
deleted file mode 100644
index 783accb582d2e23d64375c4c38d40f85942f6e37..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_818.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_813.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_814.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_815.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_816.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_817.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_818.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_818.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_818.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_818.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_819.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_819.py
deleted file mode 100644
index bf429e38b5d5a546a13461cd4db7ba4edacae167..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_819.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_807.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_810.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_811.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_812.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_818.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_819.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_819.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_819.py.debug_pkl
deleted file mode 100644
index 42d19d9ba90463e95dfac44da431fcc0284c3305..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_819.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b8aadb447ddbd657b35ca20e9c3b3eaafde15543d5cc8b89f7d46a0bb9edc94d
-size 1496
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_823.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_823.py
deleted file mode 100644
index 82e0803af0d81b8930b52242603fa885377f9e95..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_823.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_821.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_822.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_823.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_823.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_823.py.debug_pkl
deleted file mode 100644
index f1b3063e9fcde900bbeeab48d6d76248ca5b269c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_823.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fbf76ab02db4a1206f7dd8232830fad29ad2d4f73e710d40e76b67e71024c534
-size 13614
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_831.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_831.py
deleted file mode 100644
index 7a1b1c1b350eb8015f771dbc69ec4254a944b8c9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_831.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_826.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_827.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_828.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_829.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_830.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_831.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_831.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_831.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_831.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_832.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_832.py
deleted file mode 100644
index 288eb7ba9f0858c54b09d5762efdc1fef61adb59..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_832.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_820.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_823.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_824.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_825.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_831.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_832.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_832.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_832.py.debug_pkl
deleted file mode 100644
index d6e57eb6f80dc1e1e47e928cb310890a44fc1017..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_832.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5c58f9826a528641419f556822cf7da2cec4f3a2cb0514d305f91e6372de4529
-size 1496
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_836.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_836.py
deleted file mode 100644
index c22663dbcb67434b37888e7b9d5fe43aae8e2b19..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_836.py
+++ /dev/null
@@ -1,65 +0,0 @@
-class AdativeFourierNeuralOperator(Module):
-  __parameters__ = ["w1", "b1", "w2", "b2", ]
-  __buffers__ = []
-  w1 : Tensor
-  b1 : Tensor
-  w2 : Tensor
-  b2 : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  relu : __torch__.torch.nn.modules.activation.___torch_mangle_834.ReLU
-  bias : __torch__.torch.nn.modules.conv.___torch_mangle_835.Conv1d
-  def forward(self: __torch__.fcu.___torch_mangle_836.AdativeFourierNeuralOperator,
-    argument_1: Tensor) -> Tensor:
-    b2 = self.b2
-    w2 = self.w2
-    b1 = self.b1
-    w1 = self.w1
-    bias = self.bias
-    B = ops.prim.NumToTensor(torch.size(argument_1, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    N = ops.prim.NumToTensor(torch.size(argument_1, 1))
-    _4 = int(N)
-    C = ops.prim.NumToTensor(torch.size(argument_1, 2))
-    _5 = int(C)
-    _6 = int(C)
-    _7 = torch.permute(argument_1, [0, 2, 1])
-    input = torch.contiguous(_7)
-    _8 = torch.permute((bias).forward(input, ), [0, 2, 1])
-    bias0 = torch.contiguous(_8)
-    x = torch.reshape(argument_1, [_3, 336, 720, _6])
-    x0 = torch.fft_rfft2(x, None, [1, 2], "ortho")
-    _9 = ops.prim.NumToTensor(torch.size(x0, 1))
-    _10 = int(_9)
-    _11 = ops.prim.NumToTensor(torch.size(x0, 2))
-    x1 = torch.reshape(x0, [_2, _10, int(_11), 2, 64])
-    _12 = [torch.real(x1), torch.select(w1, 0, 0)]
-    _13 = torch.einsum("...bd, bdk->...bk", _12)
-    _14 = [torch.imag(x1), torch.select(w1, 0, 1)]
-    _15 = torch.einsum("...bd, bdk->...bk", _14)
-    input0 = torch.add(torch.sub(_13, _15), torch.select(b1, 0, 0))
-    _16 = torch.relu_(input0)
-    _17 = [torch.real(x1), torch.select(w1, 0, 1)]
-    _18 = torch.einsum("...bd, bdk->...bk", _17)
-    _19 = [torch.imag(x1), torch.select(w1, 0, 0)]
-    _20 = torch.einsum("...bd, bdk->...bk", _19)
-    input1 = torch.add(torch.add(_18, _20), torch.select(b1, 0, 1))
-    _21 = torch.relu_(input1)
-    _22 = torch.einsum("...bd, bdk->...bk", [_16, torch.select(w2, 0, 0)])
-    _23 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 1)])
-    x_real = torch.add(torch.sub(_22, _23), torch.select(b2, 0, 0))
-    _24 = torch.einsum("...bd, bdk->...bk", [x_real, torch.select(w2, 0, 1)])
-    _25 = torch.einsum("...bd, bdk->...bk", [_21, torch.select(w2, 0, 0)])
-    x_imag = torch.add(torch.add(_24, _25), torch.select(b2, 0, 1))
-    x2 = torch.stack([x_real, x_imag], -1)
-    x3 = torch.view_as_complex(x2)
-    _26 = ops.prim.NumToTensor(torch.size(x3, 1))
-    _27 = int(_26)
-    _28 = ops.prim.NumToTensor(torch.size(x3, 2))
-    x4 = torch.reshape(x3, [_1, _27, int(_28), 128])
-    x5 = torch.fft_irfft2(x4, [336, 720], [1, 2], "ortho")
-    x6 = torch.reshape(x5, [_0, _4, _5])
-    return torch.add(x6, bias0)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_836.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_836.py.debug_pkl
deleted file mode 100644
index a28934d5f34055ff9e5bd40a0d1d0c1e58ed7f01..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_836.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5e9e6e0437ab8e04244a6bcffa39064a007e55883c09f43b5bcb30c01512c143
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diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_844.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_844.py
deleted file mode 100644
index 92a4bc27387dab67deeeab6fdef3482b9ae8a881..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_844.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class Mlp(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  fc1 : __torch__.torch.nn.modules.linear.___torch_mangle_839.Linear
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_840.GELU
-  fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_841.Linear
-  fc3 : __torch__.torch.nn.modules.pooling.___torch_mangle_842.AdaptiveAvgPool1d
-  drop : __torch__.torch.nn.modules.dropout.___torch_mangle_843.Dropout
-  def forward(self: __torch__.fcu.___torch_mangle_844.Mlp,
-    argument_1: Tensor) -> Tensor:
-    fc3 = self.fc3
-    drop = self.drop
-    act = self.act
-    fc1 = self.fc1
-    _0 = (act).forward((fc1).forward(argument_1, ), )
-    _1 = (fc3).forward((drop).forward(_0, ), )
-    return (drop).forward1(_1, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_844.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_844.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_844.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
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diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_845.py b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_845.py
deleted file mode 100644
index 665888b60941e0ea44e778513e539481a01f40c3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_845.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class FourierNetBlock(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  normlayer1 : __torch__.torch.nn.modules.normalization.___torch_mangle_833.LayerNorm
-  filter : __torch__.fcu.___torch_mangle_836.AdativeFourierNeuralOperator
-  drop_path : __torch__.torch.nn.modules.linear.___torch_mangle_837.Identity
-  normlayer2 : __torch__.torch.nn.modules.normalization.___torch_mangle_838.LayerNorm
-  mlp : __torch__.fcu.___torch_mangle_844.Mlp
-  def forward(self: __torch__.fcu.___torch_mangle_845.FourierNetBlock,
-    argument_1: Tensor) -> Tensor:
-    mlp = self.mlp
-    normlayer2 = self.normlayer2
-    drop_path = self.drop_path
-    filter = self.filter
-    normlayer1 = self.normlayer1
-    _0 = (filter).forward((normlayer1).forward(argument_1, ), )
-    _1 = (drop_path).forward()
-    input = torch.add(argument_1, _0)
-    _2 = (mlp).forward((normlayer2).forward(input, ), )
-    _3 = (drop_path).forward1()
-    return torch.add(input, _2)
diff --git a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_845.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_845.py.debug_pkl
deleted file mode 100644
index ad854abd4e916220ccf3f7b4027240d8bd30a673..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/fcu/___torch_mangle_845.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:986065c7e22fdbe15a85a6f14df12779049483ce2c547bbacb9c035fb843c94f
-size 1496
diff --git a/xe_weights14/glonet_p3/code/__torch__/modelp2.py b/xe_weights14/glonet_p3/code/__torch__/modelp2.py
deleted file mode 100644
index c3557c182ebe2d9ad6f80fc864c64a8e9288ff08..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modelp2.py
+++ /dev/null
@@ -1,71 +0,0 @@
-class Glonet(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  jump : __torch__.modules1.residual
-  space : __torch__.sblock.FoTF
-  maps : __torch__.utils.Encoder
-  dynamics : __torch__.dblock.TeDev
-  mapsback : __torch__.utils.Decoder
-  def forward(self: __torch__.modelp2.Glonet,
-    input: Tensor) -> Tensor:
-    mapsback = self.mapsback
-    dynamics = self.dynamics
-    maps = self.maps
-    space = self.space
-    jump = self.jump
-    B = ops.prim.NumToTensor(torch.size(input, 0))
-    _0 = int(B)
-    _1 = int(B)
-    T = ops.prim.NumToTensor(torch.size(input, 1))
-    _2 = int(T)
-    _3 = int(T)
-    C = ops.prim.NumToTensor(torch.size(input, 2))
-    _4 = int(C)
-    _5 = int(C)
-    H = ops.prim.NumToTensor(torch.size(input, 3))
-    _6 = int(H)
-    _7 = int(H)
-    W = ops.prim.NumToTensor(torch.size(input, 4))
-    _8 = int(W)
-    _9 = int(W)
-    x_raw = torch.to(input, dtype=6, layout=0, device=torch.device("cuda:0"))
-    skip_feature = torch.contiguous((jump).forward(x_raw, ))
-    x_raw0 = torch.to(x_raw, dtype=6, layout=0, device=torch.device("cuda:0"))
-    spatial_feature = torch.contiguous((space).forward(x_raw0, ))
-    skip_feature0 = torch.detach(skip_feature)
-    spatial_feature0 = torch.detach(spatial_feature)
-    _10 = torch.reshape(spatial_feature0, [-1, _5, _7, _9])
-    spatial_feature1 = torch.contiguous(_10)
-    input0 = torch.to(spatial_feature1, dtype=6, layout=0, device=torch.device("cuda:0"))
-    _11, _12, = (maps).forward(input0, )
-    spatial_embed = torch.detach(_11)
-    spatial_skip_feature = torch.detach(_12)
-    spatial_embed0 = torch.contiguous(spatial_embed)
-    spatial_skip_feature0 = torch.contiguous(spatial_skip_feature)
-    C_ = ops.prim.NumToTensor(torch.size(spatial_embed0, 1))
-    _13 = int(C_)
-    _14 = int(C_)
-    H_ = ops.prim.NumToTensor(torch.size(spatial_embed0, 2))
-    _15 = int(H_)
-    _16 = int(H_)
-    W_ = ops.prim.NumToTensor(torch.size(spatial_embed0, 3))
-    _17 = int(W_)
-    _18 = torch.view(spatial_embed0, [_1, _3, _14, _16, int(W_)])
-    spatial_embed1 = torch.contiguous(_18)
-    x = torch.to(spatial_embed1, dtype=6, layout=0, device=torch.device("cuda:0"))
-    spatialtemporal_embed = torch.contiguous((dynamics).forward(x, ))
-    spatialtemporal_embed0 = torch.detach(spatialtemporal_embed)
-    _19 = [int(torch.mul(B, T)), _13, _15, _17]
-    _20 = torch.reshape(spatialtemporal_embed0, _19)
-    spatialtemporal_embed1 = torch.contiguous(_20)
-    input1 = torch.to(spatialtemporal_embed1, dtype=6, layout=0, device=torch.device("cuda:0"))
-    enc1 = torch.to(spatial_skip_feature0, dtype=6, layout=0, device=torch.device("cuda:0"))
-    predictions = torch.contiguous((mapsback).forward(input1, enc1, ))
-    predictions0 = torch.detach(predictions)
-    _21 = torch.reshape(predictions0, [_0, _2, _4, _6, _8])
-    _22 = torch.mul(torch.contiguous(_21), CONSTANTS.c0)
-    skip_feature1 = torch.to(skip_feature0, dtype=6, layout=0, device=torch.device("cuda:0"))
-    predictions1 = torch.add(_22, skip_feature1)
-    return torch.contiguous(predictions1)
diff --git a/xe_weights14/glonet_p3/code/__torch__/modelp2.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modelp2.py.debug_pkl
deleted file mode 100644
index 5b4e9b47ae85a18f356eecf18ae2d9b3866a73dd..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modelp2.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:440015bcc7b2315554718138feb4f3ce3bda68fdde863484404051ceea3ba1e5
-size 7254
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1.py b/xe_weights14/glonet_p3/code/__torch__/modules1.py
deleted file mode 100644
index 0253a936e5b3db7c4dd2ac068e45e0bcb1ce6164..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1.py
+++ /dev/null
@@ -1,222 +0,0 @@
-class residual(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  maps : __torch__.modules1.Encoder
-  mmb : __torch__.modules1.MMB
-  mapsback : __torch__.modules1.Decoder
-  def forward(self: __torch__.modules1.residual,
-    x_raw: Tensor) -> Tensor:
-    mapsback = self.mapsback
-    mmb = self.mmb
-    maps = self.maps
-    B = ops.prim.NumToTensor(torch.size(x_raw, 0))
-    _0 = int(B)
-    _1 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x_raw, 1))
-    _2 = int(T)
-    _3 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x_raw, 2))
-    _4 = int(C)
-    _5 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x_raw, 3))
-    _6 = int(H)
-    _7 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x_raw, 4))
-    _8 = int(W)
-    _9 = int(W)
-    input = torch.view(x_raw, [int(torch.mul(B, T)), _5, _7, _9])
-    _10, _11, = (maps).forward(input, )
-    C_ = ops.prim.NumToTensor(torch.size(_10, 1))
-    _12 = int(C_)
-    _13 = int(C_)
-    H_ = ops.prim.NumToTensor(torch.size(_10, 2))
-    _14 = int(H_)
-    _15 = int(H_)
-    W_ = ops.prim.NumToTensor(torch.size(_10, 3))
-    _16 = int(W_)
-    x = torch.view(_10, [_1, _3, _13, _15, int(W_)])
-    _17 = (mmb).forward(x, )
-    _18 = [int(torch.mul(B, T)), _12, _14, _16]
-    input0 = torch.reshape(_17, _18)
-    _19 = torch.reshape((mapsback).forward(input0, _11, ), [_0, _2, _4, _6, _8])
-    return _19
-class Encoder(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  enc : __torch__.torch.nn.modules.container.Sequential
-  def forward(self: __torch__.modules1.Encoder,
-    input: Tensor) -> Tuple[Tensor, Tensor]:
-    enc = self.enc
-    _3 = getattr(enc, "3")
-    enc0 = self.enc
-    _2 = getattr(enc0, "2")
-    enc1 = self.enc
-    _1 = getattr(enc1, "1")
-    enc2 = self.enc
-    _0 = getattr(enc2, "0")
-    _20 = (_0).forward(input, )
-    _21 = (_3).forward((_2).forward((_1).forward(_20, ), ), )
-    return (_21, _20)
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.BasicConv2d
-  def forward(self: __torch__.modules1.ConvSC,
-    input: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(input, )
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.GroupNorm
-  act : __torch__.torch.nn.modules.activation.LeakyReLU
-  def forward(self: __torch__.modules1.BasicConv2d,
-    input: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _22 = (norm).forward((conv).forward(input, ), )
-    return (act).forward(_22, )
-class MMB(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  enc : __torch__.torch.nn.modules.container.___torch_mangle_165.Sequential
-  dec : __torch__.torch.nn.modules.container.___torch_mangle_318.Sequential
-  def forward(self: __torch__.modules1.MMB,
-    x: Tensor) -> Tensor:
-    dec = self.dec
-    _7 = getattr(dec, "7")
-    dec0 = self.dec
-    _6 = getattr(dec0, "6")
-    dec1 = self.dec
-    _5 = getattr(dec1, "5")
-    dec2 = self.dec
-    _4 = getattr(dec2, "4")
-    dec3 = self.dec
-    _3 = getattr(dec3, "3")
-    dec4 = self.dec
-    _2 = getattr(dec4, "2")
-    dec5 = self.dec
-    _1 = getattr(dec5, "1")
-    dec6 = self.dec
-    _0 = getattr(dec6, "0")
-    enc = self.enc
-    _70 = getattr(enc, "7")
-    enc3 = self.enc
-    _60 = getattr(enc3, "6")
-    enc4 = self.enc
-    _50 = getattr(enc4, "5")
-    enc5 = self.enc
-    _40 = getattr(enc5, "4")
-    enc6 = self.enc
-    _30 = getattr(enc6, "3")
-    enc7 = self.enc
-    _20 = getattr(enc7, "2")
-    enc8 = self.enc
-    _10 = getattr(enc8, "1")
-    enc9 = self.enc
-    _00 = getattr(enc9, "0")
-    B = ops.prim.NumToTensor(torch.size(x, 0))
-    _23 = int(B)
-    _24 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x, 1))
-    _25 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x, 2))
-    _26 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x, 3))
-    _27 = int(H)
-    _28 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x, 4))
-    _29 = int(W)
-    _8 = int(W)
-    _31 = [_24, int(torch.mul(T, C)), _28, _8]
-    input = torch.reshape(x, _31)
-    _32 = (_00).forward(input, )
-    _33 = (_10).forward(_32, )
-    _34 = (_20).forward(_33, )
-    _35 = (_30).forward(_34, )
-    _36 = (_40).forward(_35, )
-    _37 = (_50).forward(_36, )
-    _38 = (_60).forward(_37, )
-    _39 = (_0).forward((_70).forward(_38, ), )
-    input1 = torch.cat([_39, _38], 1)
-    input2 = torch.cat([(_1).forward(input1, ), _37], 1)
-    input3 = torch.cat([(_2).forward(input2, ), _36], 1)
-    input4 = torch.cat([(_3).forward(input3, ), _35], 1)
-    input5 = torch.cat([(_4).forward(input4, ), _34], 1)
-    input6 = torch.cat([(_5).forward(input5, ), _33], 1)
-    input7 = torch.cat([(_6).forward(input6, ), _32], 1)
-    hid = torch.reshape((_7).forward(input7, ), [_23, _25, _26, _27, _29])
-    return hid
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_15.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_31.Sequential
-  def forward(self: __torch__.modules1.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _40 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_40, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_40, ))
-    y1 = torch.add_(y0, (_2).forward(_40, ))
-    return torch.add_(y1, (_3).forward(_40, ))
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_16.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_17.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_18.LeakyReLU
-  def forward(self: __torch__.modules1.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _41 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_41, )
-class Decoder(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  dec : __torch__.torch.nn.modules.container.___torch_mangle_338.Sequential
-  readout : __torch__.torch.nn.modules.conv.___torch_mangle_339.Conv2d
-  def forward(self: __torch__.modules1.Decoder,
-    input: Tensor,
-    argument_2: Tensor) -> Tensor:
-    readout = self.readout
-    dec = self.dec
-    _3 = getattr(dec, "3")
-    dec7 = self.dec
-    _2 = getattr(dec7, "2")
-    dec8 = self.dec
-    _1 = getattr(dec8, "1")
-    dec9 = self.dec
-    _0 = getattr(dec9, "0")
-    _42 = (_1).forward((_0).forward(input, ), )
-    input8 = torch.cat([(_2).forward(_42, ), argument_2], 1)
-    _43 = (readout).forward((_3).forward(input8, ), )
-    return _43
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1.py.debug_pkl
deleted file mode 100644
index 47c6dea4deb66805fe2fcd30dc8eb3ba51beb43e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:50905fe166107064391b579739496a97a8581d2090472f76934ced2bfe8ff94b
-size 7538
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_101.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_101.py
deleted file mode 100644
index f9a74041bab483dfe71b512f03d62ed0fcf75386..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_101.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_98.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_99.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_100.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_101.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_101.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_101.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_101.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_105.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_105.py
deleted file mode 100644
index 3601e011dc40daebd9d83fa3f501862a1531b445..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_105.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_102.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_103.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_104.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_105.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_105.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_105.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_105.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_107.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_107.py
deleted file mode 100644
index ec9589fe70863f9810dbfc9174a8df832c17d8a0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_107.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_89.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_106.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_107.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_107.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_107.py.debug_pkl
deleted file mode 100644
index 9e5c8acf1830909076edbf86f05fd086d9dae0f2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_107.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_112.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_112.py
deleted file mode 100644
index 045c34f5930bcadffff47039b92e1fd07f3abf54..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_112.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_109.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_110.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_111.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_112.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_112.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_112.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_112.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_116.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_116.py
deleted file mode 100644
index a4410c0312526fd159891f3879feb436a36af77c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_116.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_113.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_114.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_115.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_116.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_116.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_116.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_116.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_120.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_120.py
deleted file mode 100644
index 14e59d5ea63a5ec923d146d22a44fad132a83cec..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_120.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_117.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_118.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_119.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_120.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_120.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_120.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_120.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_124.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_124.py
deleted file mode 100644
index 8abb6802f44aa021056e2f53e90ea08b30c363f0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_124.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_121.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_122.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_123.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_124.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_124.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_124.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_124.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_126.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_126.py
deleted file mode 100644
index c8c40d24db8db81b213bf0b3785ed305aa681273..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_126.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_108.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_125.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_126.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_126.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_126.py.debug_pkl
deleted file mode 100644
index 2e57e10dfd16dfe8e724560385b8d7fae5c1545e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_126.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_13.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_13.py
deleted file mode 100644
index 064575a7f51c8add32bb5447ac447876a1cb378e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_13.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_10.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_11.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_12.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_13.BasicConv2d,
-    argument_1: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_13.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_13.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_13.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_131.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_131.py
deleted file mode 100644
index 518b47eff50c07ab7f644e68c95d92320e9c992b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_131.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_128.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_129.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_130.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_131.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_131.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_131.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_131.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_135.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_135.py
deleted file mode 100644
index 1e49a731aac1fe53cfe3fb31b3c49d12feb604d9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_135.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_132.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_133.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_134.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_135.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_135.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_135.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_135.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_139.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_139.py
deleted file mode 100644
index 2b0089046aa10beb64dabea83d3692f57cd7381e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_139.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_136.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_137.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_138.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_139.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_139.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_139.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_139.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_14.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_14.py
deleted file mode 100644
index 58e0e7b8ca680701c9bfc7a826397fd73f6c195a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_14.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_13.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_14.ConvSC,
-    argument_1: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(argument_1, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_14.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_14.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_14.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_143.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_143.py
deleted file mode 100644
index 698d939739721f3790ea914e70de543e2bf04a98..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_143.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_140.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_141.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_142.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_143.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_143.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_143.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_143.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_145.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_145.py
deleted file mode 100644
index 0a37ecead870dc33645db34e215c419eae64fd68..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_145.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_127.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_144.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_145.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_145.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_145.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_145.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_150.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_150.py
deleted file mode 100644
index dd8aa7cf1be9261e6a87caa797c3ed3f70171bbe..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_150.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_147.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_148.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_149.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_150.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_150.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_150.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_150.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_154.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_154.py
deleted file mode 100644
index 272b4264d3bd3dea37b08581b6e999fd38ee09f2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_154.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_151.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_152.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_153.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_154.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_154.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_154.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_154.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_158.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_158.py
deleted file mode 100644
index 4dab7d854d992394ada0784366de5b520c3ebc9b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_158.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_155.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_156.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_157.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_158.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_158.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_158.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_158.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_162.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_162.py
deleted file mode 100644
index 3a0d823f1576df444a778d6935cf1eed5d088eed..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_162.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_159.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_160.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_161.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_162.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_162.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_162.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_162.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_164.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_164.py
deleted file mode 100644
index 6ba1d16ff09ccabd1114829e516bed268a6dedec..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_164.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_146.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_163.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_164.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_164.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_164.py.debug_pkl
deleted file mode 100644
index e187b8043fba3035fbc4f77da9417882cee1cece..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_164.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_170.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_170.py
deleted file mode 100644
index d56d33dd83c49e39d1c8eceb3663325cde990fcb..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_170.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_167.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_168.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_169.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_170.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_170.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_170.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_170.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_174.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_174.py
deleted file mode 100644
index 5aa4b48ef95dbd0058b73b6d28de792aa3dbfb9a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_174.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_171.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_172.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_173.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_174.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_174.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_174.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_174.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_178.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_178.py
deleted file mode 100644
index 1dfa1eae8acfc0a33e41b729e4478d75d31dc07c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_178.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_175.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_176.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_177.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_178.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_178.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_178.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_178.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_182.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_182.py
deleted file mode 100644
index 3ca80905b52b486b4b51513488830889ba33004c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_182.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_179.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_180.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_181.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_182.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_182.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_182.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_182.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_184.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_184.py
deleted file mode 100644
index c79abd3ffeb8159a22f92954e0f6736372ae1d62..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_184.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_166.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_183.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_184.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_184.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_184.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_184.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_189.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_189.py
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index 374918f83e34e3f1f2669fe7f1d6b42c4cf42673..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_189.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_186.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_187.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_188.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_189.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_189.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_189.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_189.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_193.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_193.py
deleted file mode 100644
index 27f50ba5eac3b4c3aafd7414120ee20819c243e0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_193.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_190.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_191.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_192.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_193.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_193.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_193.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_193.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_197.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_197.py
deleted file mode 100644
index 8b5d782cd0a887423ecb3212861e106bf4b1a8e4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_197.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_194.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_195.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_196.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_197.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_197.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_197.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_197.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_201.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_201.py
deleted file mode 100644
index 0fe8376b95a9b76d1ed68593ede48e39d9bbe3d4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_201.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_198.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_199.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_200.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_201.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_201.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_201.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_201.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_203.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_203.py
deleted file mode 100644
index 97ba5f416b3bc47cdda06fa2c91d3f15c5a75ea4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_203.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_185.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_202.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_203.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_203.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_203.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_203.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_208.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_208.py
deleted file mode 100644
index 5f3576d4f0499629cb1887c385fea5ce1eec495f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_208.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_205.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_206.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_207.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_208.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_208.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_208.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_208.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_212.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_212.py
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index a0d3136751f2bd71fe0d42d26a80c2c5bbc7d4ce..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_212.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_209.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_210.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_211.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_212.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_212.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_212.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_212.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_216.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_216.py
deleted file mode 100644
index 288cbc5c962c6d8984367ff15eb2d68468d6520b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_216.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_213.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_214.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_215.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_216.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_216.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_216.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_216.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_22.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_22.py
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index ccd37a44494ff0ee6e524541b428ceb54be1c1d9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_22.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_19.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_20.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_21.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_22.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_22.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_22.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_22.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_220.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_220.py
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index d86a5e3c8578b29e9a03886f02a33d8767be1701..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_220.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_217.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_218.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_219.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_220.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_220.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_220.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_220.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_222.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_222.py
deleted file mode 100644
index c471b1e09c8557dfaf689f5ab996f4d0cf5fba33..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_222.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_204.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_221.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_222.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_222.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_222.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_222.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_227.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_227.py
deleted file mode 100644
index 473f29ff30984002139b3dd6571cdbeadf9bbdfe..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_227.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_224.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_225.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_226.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_227.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_227.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_227.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_227.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_231.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_231.py
deleted file mode 100644
index 8bcf3690c09edb8e909b0812fedf35cef328d348..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_231.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_228.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_229.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_230.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_231.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_231.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_231.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_231.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_235.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_235.py
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_235.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_232.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_233.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_234.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_235.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_235.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_235.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_235.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_239.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_239.py
deleted file mode 100644
index cca638271d6ff60b320a3e7e5f1546a23ffa29e3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_239.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_236.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_237.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_238.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_239.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_239.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_239.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_239.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_241.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_241.py
deleted file mode 100644
index 842854ff37d8c71a0bbf8040323c3197e2eaf9fc..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_241.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_223.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_240.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_241.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_241.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_241.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_241.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_246.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_246.py
deleted file mode 100644
index e7fd8cb4a952e44b9320abcb8d9fec145872ebcd..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_246.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_243.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_244.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_245.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_246.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_246.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_246.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_246.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_250.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_250.py
deleted file mode 100644
index 1156b53d651b031c2fe92ae82984ceba9b525396..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_250.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_247.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_248.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_249.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_250.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_250.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_250.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_250.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_254.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_254.py
deleted file mode 100644
index 2cb2fecb3218be19bf1f641066d76b904641c5a5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_254.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_251.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_252.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_253.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_254.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_254.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_254.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_254.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_258.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_258.py
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index ac175524a5759a7a70f595be15ce1ada11b8189e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_258.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_255.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_256.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_257.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_258.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_258.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_258.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_258.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_26.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_26.py
deleted file mode 100644
index 3abdac87068ff67ba13a4d262f53bd92cfd73de4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_26.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_23.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_24.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_25.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_26.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_26.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_26.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_26.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_260.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_260.py
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index 21115b74a3cafbe83614b2a3b08147edf3ae1aa2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_260.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_242.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_259.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_260.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_260.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_260.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_260.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_265.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_265.py
deleted file mode 100644
index 991b334cb1f77cbaf128df235f41c001d91c2fed..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_265.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_262.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_263.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_264.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_265.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_265.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_265.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_265.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_269.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_266.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_267.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_268.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_269.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_269.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_269.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_273.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_273.py
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_273.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_270.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_271.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_272.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_273.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_273.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_273.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_277.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_277.py
deleted file mode 100644
index 73c383f84086c390fe54578e76b2e3234e48c0cd..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_277.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_274.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_275.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_276.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_277.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_277.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_277.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_277.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_279.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_279.py
deleted file mode 100644
index b3ec377474b06574f47aac871804128acceec749..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_279.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_261.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_278.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_279.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_279.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_279.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_279.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_284.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_284.py
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index 8e891aed554d8e6169a0b5c254b5a7847e9cbe26..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_284.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_281.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_282.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_283.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_284.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_284.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_284.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_284.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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index 5581a37dffb35d99a20d8e6d84a88abae32b1bb2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_288.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_285.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_286.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_287.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_288.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_288.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_288.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_288.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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index 0a83bdd032a7f8cf4a6433e670184699689855a1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_292.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_289.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_290.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_291.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_292.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_292.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_292.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_292.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_296.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_296.py
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_296.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_293.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_294.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_295.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_296.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_296.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_296.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_296.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_298.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_298.py
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index 66beca1261889e9c74c6e2dfb806c43388fbeebb..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_298.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_280.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_297.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_298.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_298.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_298.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_298.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_3.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_3.py
deleted file mode 100644
index 6bc3334e0ca9d63087543b1bc9430ff113c06abe..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_3.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_0.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_1.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_2.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_3.BasicConv2d,
-    argument_1: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_3.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_3.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_3.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_30.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_30.py
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_30.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_27.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_28.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_29.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_30.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_30.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_30.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_30.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_303.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_303.py
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_303.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_300.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_301.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_302.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_303.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_303.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_303.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_307.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_307.py
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_307.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_304.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_305.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_306.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_307.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_307.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_307.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_311.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_311.py
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_311.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_308.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_309.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_310.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_311.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_311.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_311.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_311.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_315.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_315.py
deleted file mode 100644
index 5f6508b1402985003103c183420b1cb8c43d6d86..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_315.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_312.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_313.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_314.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_315.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_315.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_315.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_315.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_317.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_317.py
deleted file mode 100644
index 3e924d467ac042f8518a8c0fc0c762123946a953..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_317.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_299.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_316.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_317.Inception,
-    input: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(input, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_317.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_317.py.debug_pkl
deleted file mode 100644
index 5f3b75153c9fa15a99394997638cd1eedf890c69..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_317.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_321.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_321.py
deleted file mode 100644
index 14f6ee47df8fe7509d7518ee9c60872fcae33e55..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_321.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.ConvTranspose2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_319.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_320.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_321.BasicConv2d,
-    input: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(input, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_321.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_321.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_321.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_322.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_322.py
deleted file mode 100644
index 2287c6e69e49765c24b8fab9e2ecdb32edbc04c3..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_322.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_321.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_322.ConvSC,
-    input: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(input, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_322.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_322.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_322.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_326.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_326.py
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index 7360d1f5e5c7ce86597d8e9fd84e6087fc4d63e5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_326.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_323.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_324.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_325.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_326.BasicConv2d,
-    argument_1: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_326.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_326.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_326.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_327.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_327.py
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index 8e457b1783c2c4d11979a4fedb23e28b16a88e21..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_327.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_326.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_327.ConvSC,
-    argument_1: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(argument_1, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_327.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_327.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_327.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_331.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_331.py
deleted file mode 100644
index 7fb5bd11ca79ede58dcf502088dc1055ea9bf3b0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_331.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_328.ConvTranspose2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_329.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_330.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_331.BasicConv2d,
-    argument_1: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_331.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_331.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_331.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_332.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_332.py
deleted file mode 100644
index bc366373b54165ad6d9045a5b0f82d1de2b8e462..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_332.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_331.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_332.ConvSC,
-    argument_1: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(argument_1, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_332.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_332.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_332.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_336.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_336.py
deleted file mode 100644
index 200b3a69fe74b4fe41a6d14591334a91b8235b98..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_336.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_333.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_334.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_335.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_336.BasicConv2d,
-    input: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(input, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_336.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_336.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_336.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_337.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_337.py
deleted file mode 100644
index bce89e18ade0fc09cf1b562e8cbf74ffdb944ee7..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_337.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_336.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_337.ConvSC,
-    input: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(input, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_337.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_337.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_337.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_36.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_36.py
deleted file mode 100644
index 10c34c7a7f6369fd3f99c6d327662472643bcfb0..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_36.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_33.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_34.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_35.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_36.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_36.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_36.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_36.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_4.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_4.py
deleted file mode 100644
index 9fd0b4d1504046d65339d8e18be9189b99080304..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_4.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_3.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_4.ConvSC,
-    argument_1: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(argument_1, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_4.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_4.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_4.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_40.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_40.py
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index a8aac2c1d7bc5b63f17f7a12355159994c7f5e22..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_40.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_37.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_38.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_39.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_40.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_40.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_40.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_40.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_44.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_44.py
deleted file mode 100644
index fe9773f8c93e2451d9d3cebc303cddb47b6b6ea2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_44.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_41.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_42.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_43.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_44.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_44.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_44.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_44.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_48.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_48.py
deleted file mode 100644
index 9dfbbdc31ccbcfde75395692c2ad026a0ba2fb7d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_48.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_45.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_46.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_47.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_48.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_48.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_48.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_48.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_50.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_50.py
deleted file mode 100644
index ba7b1ccbc0b2b8fff115bd3bc509a872da93752d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_50.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_32.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_49.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_50.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_50.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_50.py.debug_pkl
deleted file mode 100644
index 3873a44644fa0928508980fef9486c4433ef219f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_50.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_55.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_55.py
deleted file mode 100644
index 512fbfd10bf28e8cc95c06ee5a9f72d863048ea9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_55.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_52.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_53.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_54.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_55.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_55.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_55.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_55.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_59.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_59.py
deleted file mode 100644
index e9b399310d1dbc67074ba45823c358459c506c9d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_59.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_56.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_57.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_58.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_59.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_59.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_59.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_59.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_63.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_63.py
deleted file mode 100644
index 675d8585333b4647332e1f5af630729b0dd9419b..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_63.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_60.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_61.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_62.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_63.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_63.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_63.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_63.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_67.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_67.py
deleted file mode 100644
index 32c6ff38bc16d606aeb7c8b31f83871d4723f4ec..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_67.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_64.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_65.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_66.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_67.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_67.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_67.py.debug_pkl
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index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_67.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_69.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_69.py
deleted file mode 100644
index cd0727b173cf797925450a629d4a1b3a89d83183..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_69.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_51.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_68.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_69.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_69.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_69.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_69.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_74.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_74.py
deleted file mode 100644
index 00276b44c82a8c1c2573e2845d189ee2c523ee90..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_74.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_71.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_72.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_73.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_74.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_74.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_74.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_74.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_78.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_78.py
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index c384b4f149c93286bacae968a04e5f35b905baec..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_78.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_75.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_76.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_77.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_78.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_78.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_78.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_78.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_8.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_8.py
deleted file mode 100644
index 00185510f0e8482046c4576bbf4c791728b1a8b9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_8.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_5.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_6.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_7.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_8.BasicConv2d,
-    argument_1: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_8.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_8.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_8.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_82.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_82.py
deleted file mode 100644
index 976456dc0867d1a4d07bb9df72f712d7b7f6d21d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_82.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_79.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_80.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_81.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_82.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_82.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_82.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_82.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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-size 106
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_86.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_86.py
deleted file mode 100644
index e7c230a56d3f87a5f32cf616fb8e46ff1b3400b4..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_86.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_83.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_84.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_85.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_86.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_86.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_86.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_86.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_88.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_88.py
deleted file mode 100644
index b1ae7973553d0e01af3b1461a923850bab494924..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_88.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class Inception(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_70.Conv2d
-  lls : __torch__.torch.nn.modules.container.___torch_mangle_87.Sequential
-  def forward(self: __torch__.modules1.___torch_mangle_88.Inception,
-    argument_1: Tensor) -> Tensor:
-    lls = self.lls
-    _3 = getattr(lls, "3")
-    lls0 = self.lls
-    _2 = getattr(lls0, "2")
-    lls1 = self.lls
-    _1 = getattr(lls1, "1")
-    lls2 = self.lls
-    _0 = getattr(lls2, "0")
-    conv1 = self.conv1
-    _4 = (conv1).forward(argument_1, )
-    y = torch.add((_0).forward(_4, ), CONSTANTS.c1)
-    y0 = torch.add_(y, (_1).forward(_4, ))
-    y1 = torch.add_(y0, (_2).forward(_4, ))
-    return torch.add_(y1, (_3).forward(_4, ))
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_88.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_88.py.debug_pkl
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index 32c98e3243efa1aebacec199e8fbc8c51a1b030d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_88.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:63b92b75bbeefba7a855309d5c66c6083dea100d30df0e815a04c535a72396fb
-size 1891
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_9.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_9.py
deleted file mode 100644
index 5a53ffe2f99d9028ca2a22f47e152cde5ca5a71f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_9.py
+++ /dev/null
@@ -1,10 +0,0 @@
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.modules1.___torch_mangle_8.BasicConv2d
-  def forward(self: __torch__.modules1.___torch_mangle_9.ConvSC,
-    argument_1: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(argument_1, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_9.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_9.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_9.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_93.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_93.py
deleted file mode 100644
index 1c3d286d216a35b6cd22e3058e70c93177b5fab5..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_93.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_90.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_91.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_92.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_93.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_93.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_93.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_93.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
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diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_97.py b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_97.py
deleted file mode 100644
index 6c883d28565e3f8be6742020e622cb9d0870d706..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_97.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class GroupConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_94.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_95.GroupNorm
-  activate : __torch__.torch.nn.modules.activation.___torch_mangle_96.LeakyReLU
-  def forward(self: __torch__.modules1.___torch_mangle_97.GroupConv2d,
-    argument_1: Tensor) -> Tensor:
-    activate = self.activate
-    norm = self.norm
-    conv = self.conv
-    _0 = (norm).forward((conv).forward(argument_1, ), )
-    return (activate).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_97.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_97.py.debug_pkl
deleted file mode 100644
index 2e503d2d6e4ef66bb71d2d07a6e5595b8d575638..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/modules1/___torch_mangle_97.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:326da525ff54021be4bbeb601c3ff4ee74e0cee4e45a0fbe1b9ccde3ca16229c
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diff --git a/xe_weights14/glonet_p3/code/__torch__/sblock.py b/xe_weights14/glonet_p3/code/__torch__/sblock.py
deleted file mode 100644
index d652501e497dc7f2ff9fb22c85ea6d27f2ac5ec1..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/sblock.py
+++ /dev/null
@@ -1,174 +0,0 @@
-class FoTF(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  NN : __torch__.NN.Local_CNN_Branch
-  NO : __torch__.NO.GF_Block
-  up : __torch__.torch.nn.modules.conv.___torch_mangle_529.ConvTranspose2d
-  down : __torch__.torch.nn.modules.conv.___torch_mangle_530.Conv2d
-  conv1x1 : __torch__.torch.nn.modules.conv.___torch_mangle_531.Conv2d
-  def forward(self: __torch__.sblock.FoTF,
-    x_raw: Tensor) -> Tensor:
-    down = self.down
-    conv1x1 = self.conv1x1
-    up = self.up
-    NN = self.NN
-    NO = self.NO
-    B = ops.prim.NumToTensor(torch.size(x_raw, 0))
-    _0 = int(B)
-    _1 = int(B)
-    _2 = int(B)
-    _3 = int(B)
-    _4 = int(B)
-    _5 = int(B)
-    _6 = int(B)
-    _7 = int(B)
-    _8 = int(B)
-    _9 = int(B)
-    _10 = int(B)
-    _11 = int(B)
-    T = ops.prim.NumToTensor(torch.size(x_raw, 1))
-    _12 = int(T)
-    _13 = int(T)
-    _14 = int(T)
-    _15 = int(T)
-    _16 = int(T)
-    _17 = int(T)
-    _18 = int(T)
-    _19 = int(T)
-    _20 = int(T)
-    _21 = int(T)
-    _22 = int(T)
-    _23 = int(T)
-    C = ops.prim.NumToTensor(torch.size(x_raw, 2))
-    _24 = int(C)
-    _25 = int(C)
-    _26 = int(C)
-    _27 = int(C)
-    _28 = int(C)
-    _29 = int(C)
-    _30 = int(C)
-    _31 = int(C)
-    _32 = int(C)
-    _33 = int(C)
-    _34 = int(C)
-    _35 = int(C)
-    _36 = int(C)
-    _37 = int(C)
-    _38 = int(C)
-    _39 = int(C)
-    _40 = int(C)
-    _41 = int(C)
-    _42 = int(C)
-    _43 = int(C)
-    _44 = int(C)
-    _45 = int(C)
-    _46 = int(C)
-    _47 = int(C)
-    H = ops.prim.NumToTensor(torch.size(x_raw, 3))
-    _48 = int(H)
-    _49 = int(H)
-    _50 = int(H)
-    _51 = int(H)
-    _52 = int(H)
-    _53 = int(H)
-    _54 = int(H)
-    _55 = int(H)
-    _56 = int(H)
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-    _59 = int(H)
-    _60 = int(H)
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-    _62 = int(H)
-    _63 = int(H)
-    _64 = int(H)
-    _65 = int(H)
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-    _68 = int(H)
-    _69 = int(H)
-    _70 = int(H)
-    _71 = int(H)
-    W = ops.prim.NumToTensor(torch.size(x_raw, 4))
-    _72 = int(W)
-    _73 = int(W)
-    _74 = int(W)
-    _75 = int(W)
-    _76 = int(W)
-    _77 = int(W)
-    _78 = int(W)
-    _79 = int(W)
-    _80 = int(W)
-    _81 = int(W)
-    _82 = int(W)
-    _83 = int(W)
-    _84 = int(W)
-    _85 = int(W)
-    _86 = int(W)
-    _87 = int(W)
-    _88 = int(W)
-    _89 = int(W)
-    _90 = int(W)
-    _91 = int(W)
-    _92 = int(W)
-    _93 = int(W)
-    _94 = int(W)
-    _95 = int(W)
-    _96 = (NO).forward(x_raw, )
-    _97 = (NN).forward(x_raw, )
-    _98 = [int(torch.mul(B, T)), _47, _71, _95]
-    input = torch.view(_96, _98)
-    gf_features_up = torch.view((up).forward(input, ), [_11, _23, _46, _70, _94])
-    _99 = [int(torch.mul(B, T)), _45, _69, _93]
-    input0 = torch.view(_97, _99)
-    lc_features = torch.view((conv1x1).forward(input0, ), [_10, _22, _44, _68, _92])
-    x = torch.add(gf_features_up, lc_features)
-    _100 = (NO).forward1(x, )
-    _101 = (NN).forward1(x, )
-    _102 = [int(torch.mul(B, T)), _43, _67, _91]
-    input1 = torch.view(_100, _102)
-    gf_features_down = torch.view((down).forward(input1, ), [_9, _21, _42, _66, _90])
-    _103 = [int(torch.mul(B, T)), _41, _65, _89]
-    input2 = torch.view(_101, _103)
-    lc_features0 = torch.view((conv1x1).forward1(input2, ), [_8, _20, _40, _64, _88])
-    x0 = torch.add(gf_features_down, lc_features0)
-    _104 = (NO).forward2(x0, )
-    _105 = (NN).forward2(x0, )
-    _106 = [int(torch.mul(B, T)), _39, _63, _87]
-    input3 = torch.view(_104, _106)
-    gf_features_up0 = torch.view((up).forward1(input3, ), [_7, _19, _38, _62, _86])
-    _107 = [int(torch.mul(B, T)), _37, _61, _85]
-    input4 = torch.view(_105, _107)
-    lc_features1 = torch.view((conv1x1).forward2(input4, ), [_6, _18, _36, _60, _84])
-    x1 = torch.add(gf_features_up0, lc_features1)
-    _108 = (NO).forward3(x1, )
-    _109 = (NN).forward3(x1, )
-    _110 = [int(torch.mul(B, T)), _35, _59, _83]
-    input5 = torch.view(_108, _110)
-    gf_features_down0 = torch.view((down).forward1(input5, ), [_5, _17, _34, _58, _82])
-    _111 = [int(torch.mul(B, T)), _33, _57, _81]
-    input6 = torch.view(_109, _111)
-    lc_features2 = torch.view((conv1x1).forward3(input6, ), [_4, _16, _32, _56, _80])
-    x2 = torch.add(gf_features_down0, lc_features2)
-    _112 = (NO).forward4(x2, )
-    _113 = (NN).forward4(x2, )
-    _114 = [int(torch.mul(B, T)), _31, _55, _79]
-    input7 = torch.view(_112, _114)
-    gf_features_up1 = torch.view((up).forward2(input7, ), [_3, _15, _30, _54, _78])
-    _115 = [int(torch.mul(B, T)), _29, _53, _77]
-    input8 = torch.view(_113, _115)
-    lc_features3 = torch.view((conv1x1).forward4(input8, ), [_2, _14, _28, _52, _76])
-    x3 = torch.add(gf_features_up1, lc_features3)
-    _116 = (NO).forward5(x3, )
-    _117 = (NN).forward5(x3, )
-    _118 = [int(torch.mul(B, T)), _27, _51, _75]
-    input9 = torch.view(_116, _118)
-    gf_features_down1 = torch.view((down).forward2(input9, ), [_1, _13, _26, _50, _74])
-    _119 = [int(torch.mul(B, T)), _25, _49, _73]
-    input10 = torch.view(_117, _119)
-    lc_features4 = torch.view((conv1x1).forward5(input10, ), [_0, _12, _24, _48, _72])
-    x4 = torch.add(gf_features_down1, lc_features4)
-    _120 = torch.add((NO).forward6(x4, ), (NN).forward6(x4, ))
-    return _120
diff --git a/xe_weights14/glonet_p3/code/__torch__/sblock.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/sblock.py.debug_pkl
deleted file mode 100644
index c256e70fe8da9f195d7e4dfaff97cb34646cadef..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/sblock.py.debug_pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:90903e07c71f12b4dddf1b05c2a371d0ce710c9677026fa706f2953133528117
-size 6610
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation.py
deleted file mode 100644
index 80aaecfceef2cc9741ccc2fe1317fa7b0a60de6d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation.py
+++ /dev/null
@@ -1,74 +0,0 @@
-class LeakyReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    input = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return input
-class ReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-class MultiheadAttention(Module):
-  __parameters__ = ["in_proj_weight", "in_proj_bias", ]
-  __buffers__ = []
-  in_proj_weight : Tensor
-  in_proj_bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  out_proj : __torch__.torch.nn.modules.linear.NonDynamicallyQuantizableLinear
-class GELU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward2(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward4(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward6(self: __torch__.torch.nn.modules.activation.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-class Tanh(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward2(self: __torch__.torch.nn.modules.activation.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward4(self: __torch__.torch.nn.modules.activation.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward6(self: __torch__.torch.nn.modules.activation.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_100.py
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@@ -1,9 +0,0 @@
-class LeakyReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_100.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
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-class LeakyReLU(Module):
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-    return hid
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-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
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-    return embed
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_149.py
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-class LeakyReLU(Module):
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_149.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_149.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_149.py.debug_pkl
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-class LeakyReLU(Module):
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-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_153.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_153.py.debug_pkl
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-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_157.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_157.py.debug_pkl
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-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_18.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_18.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_181.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_181.py.debug_pkl
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-    input = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return input
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-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_200.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_200.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_207.py
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-class LeakyReLU(Module):
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_207.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_207.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_249.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_249.py.debug_pkl
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_257.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_257.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_257.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_264.py
+++ /dev/null
@@ -1,9 +0,0 @@
-class LeakyReLU(Module):
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_264.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_264.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_264.py.debug_pkl
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-class LeakyReLU(Module):
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_29.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_29.py.debug_pkl
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-class LeakyReLU(Module):
-  __parameters__ = []
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_320.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    input = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return input
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-class LeakyReLU(Module):
-  __parameters__ = []
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_325.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    input = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return input
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-    return hid
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-class ReLU(Module):
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-class LeakyReLU(Module):
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_35.LeakyReLU,
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-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
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-class MultiheadAttention(Module):
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-class GELU(Module):
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-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_356.GELU,
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_356.GELU,
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-    return torch.gelu(argument_1)
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-class ReLU(Module):
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-class MultiheadAttention(Module):
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-class GELU(Module):
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_372.GELU,
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-    return torch.gelu(argument_1)
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-class GELU(Module):
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-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward2(self: __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward4(self: __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_388.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_388.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_388.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_39.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_39.py
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_39.py
+++ /dev/null
@@ -1,9 +0,0 @@
-class LeakyReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_39.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_39.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_39.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_395.py
+++ /dev/null
@@ -1,5 +0,0 @@
-class ReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_395.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_395.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_400.py
+++ /dev/null
@@ -1,8 +0,0 @@
-class MultiheadAttention(Module):
-  __parameters__ = ["in_proj_weight", "in_proj_bias", ]
-  __buffers__ = []
-  in_proj_weight : Tensor
-  in_proj_bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  out_proj : __torch__.torch.nn.modules.linear.___torch_mangle_399.NonDynamicallyQuantizableLinear
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_404.py
+++ /dev/null
@@ -1,26 +0,0 @@
-class GELU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_404.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.___torch_mangle_404.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward2(self: __torch__.torch.nn.modules.activation.___torch_mangle_404.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_404.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward4(self: __torch__.torch.nn.modules.activation.___torch_mangle_404.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_404.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_404.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_404.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_404.py.debug_pkl
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-class ReLU(Module):
-  __parameters__ = []
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-  training : bool
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_411.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_411.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_416.py
+++ /dev/null
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-class MultiheadAttention(Module):
-  __parameters__ = ["in_proj_weight", "in_proj_bias", ]
-  __buffers__ = []
-  in_proj_weight : Tensor
-  in_proj_bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  out_proj : __torch__.torch.nn.modules.linear.___torch_mangle_415.NonDynamicallyQuantizableLinear
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_420.py
+++ /dev/null
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-class GELU(Module):
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-  __buffers__ = []
-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_420.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.___torch_mangle_420.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward2(self: __torch__.torch.nn.modules.activation.___torch_mangle_420.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_420.GELU,
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-    return torch.gelu(argument_1)
-  def forward4(self: __torch__.torch.nn.modules.activation.___torch_mangle_420.GELU,
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-    return torch.gelu(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_420.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_420.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_420.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_420.py.debug_pkl
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-class ReLU(Module):
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_427.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_427.py.debug_pkl
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-class LeakyReLU(Module):
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_43.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_43.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_43.py.debug_pkl
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-class MultiheadAttention(Module):
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-    return torch.gelu(argument_1)
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-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_436.GELU,
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-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_436.GELU,
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_436.GELU,
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-    return torch.gelu(argument_1)
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-class ReLU(Module):
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+++ /dev/null
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-class MultiheadAttention(Module):
-  __parameters__ = ["in_proj_weight", "in_proj_bias", ]
-  __buffers__ = []
-  in_proj_weight : Tensor
-  in_proj_bias : Tensor
-  training : bool
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-class GELU(Module):
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_452.GELU,
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_452.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
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-class ReLU(Module):
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-class MultiheadAttention(Module):
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-  training : bool
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-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_468.GELU,
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_47.LeakyReLU,
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-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
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-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU,
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-    return torch.gelu(argument_1)
-  def forward4(self: __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_500.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_500.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_500.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_507.py
+++ /dev/null
@@ -1,5 +0,0 @@
-class ReLU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_507.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_507.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_512.py
+++ /dev/null
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-class MultiheadAttention(Module):
-  __parameters__ = ["in_proj_weight", "in_proj_bias", ]
-  __buffers__ = []
-  in_proj_weight : Tensor
-  in_proj_bias : Tensor
-  training : bool
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-  out_proj : __torch__.torch.nn.modules.linear.___torch_mangle_511.NonDynamicallyQuantizableLinear
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_516.py
+++ /dev/null
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-class GELU(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward2(self: __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward4(self: __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_516.GELU,
-    argument_1: Tensor) -> Tensor:
-    return torch.gelu(argument_1)
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/activation/___torch_mangle_525.py
+++ /dev/null
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-class Tanh(Module):
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-  __buffers__ = []
-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward1(self: __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward2(self: __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh,
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-    return torch.tanh(argument_1)
-  def forward3(self: __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward4(self: __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward5(self: __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
-  def forward6(self: __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh,
-    argument_1: Tensor) -> Tensor:
-    return torch.tanh(argument_1)
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-class LeakyReLU(Module):
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_534.LeakyReLU,
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-    input = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return input
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-class LeakyReLU(Module):
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_537.LeakyReLU,
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-    spatial_embed = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return spatial_embed
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_54.LeakyReLU,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.leaky_relu_(argument_1, 0.20000000000000001)
-    return _0
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-    return _0
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-class ReLU(Module):
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_762.GELU,
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-    return torch.gelu(argument_1)
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_77.LeakyReLU,
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-  def forward(self: __torch__.torch.nn.modules.activation.___torch_mangle_840.GELU,
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-    return torch.gelu(argument_1)
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+++ /dev/null
@@ -1,13 +0,0 @@
-class Sequential(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  __annotations__["0"] = __torch__.modules1.___torch_mangle_184.Inception
-  __annotations__["1"] = __torch__.modules1.___torch_mangle_203.Inception
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-  __annotations__["7"] = __torch__.modules1.___torch_mangle_317.Inception
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_318.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_318.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_318.py.debug_pkl
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_338.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_338.py
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index ba25c526b83f404d2b23f5725eefbc1d935ae2b9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_338.py
+++ /dev/null
@@ -1,9 +0,0 @@
-class Sequential(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  __annotations__["0"] = __torch__.modules1.___torch_mangle_322.ConvSC
-  __annotations__["1"] = __torch__.modules1.___torch_mangle_327.ConvSC
-  __annotations__["2"] = __torch__.modules1.___torch_mangle_332.ConvSC
-  __annotations__["3"] = __torch__.modules1.___torch_mangle_337.ConvSC
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_338.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_338.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_338.py.debug_pkl
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index f59d55ff033ee70f9a03a39daa29896917a40a3a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_49.py
+++ /dev/null
@@ -1,9 +0,0 @@
-class Sequential(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  __annotations__["0"] = __torch__.modules1.___torch_mangle_36.GroupConv2d
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-  __annotations__["3"] = __torch__.modules1.___torch_mangle_48.GroupConv2d
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_49.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_49.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_527.py
+++ /dev/null
@@ -1,80 +0,0 @@
-class Sequential(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  transposeconv1 : __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d
-  act1 : __torch__.torch.nn.modules.activation.Tanh
-  transposeconv2 : __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d
-  act2 : __torch__.torch.nn.modules.activation.___torch_mangle_525.Tanh
-  transposeconv3 : __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d
-  def forward(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
-    input: Tensor) -> Tensor:
-    transposeconv3 = self.transposeconv3
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-    transposeconv2 = self.transposeconv2
-    act1 = self.act1
-    transposeconv1 = self.transposeconv1
-    _0 = (act1).forward((transposeconv1).forward(input, ), )
-    _1 = (act2).forward((transposeconv2).forward(_0, ), )
-    return (transposeconv3).forward(_1, )
-  def forward1(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
-    input: Tensor) -> Tensor:
-    transposeconv3 = self.transposeconv3
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-    transposeconv1 = self.transposeconv1
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-    _3 = (act2).forward1((transposeconv2).forward1(_2, ), )
-    return (transposeconv3).forward1(_3, )
-  def forward2(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
-    input: Tensor) -> Tensor:
-    transposeconv3 = self.transposeconv3
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-    transposeconv1 = self.transposeconv1
-    _4 = (act1).forward2((transposeconv1).forward2(input, ), )
-    _5 = (act2).forward2((transposeconv2).forward2(_4, ), )
-    return (transposeconv3).forward2(_5, )
-  def forward3(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
-    input: Tensor) -> Tensor:
-    transposeconv3 = self.transposeconv3
-    act2 = self.act2
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-    act1 = self.act1
-    transposeconv1 = self.transposeconv1
-    _6 = (act1).forward3((transposeconv1).forward3(input, ), )
-    _7 = (act2).forward3((transposeconv2).forward3(_6, ), )
-    return (transposeconv3).forward3(_7, )
-  def forward4(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
-    input: Tensor) -> Tensor:
-    transposeconv3 = self.transposeconv3
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-    act1 = self.act1
-    transposeconv1 = self.transposeconv1
-    _8 = (act1).forward4((transposeconv1).forward4(input, ), )
-    _9 = (act2).forward4((transposeconv2).forward4(_8, ), )
-    return (transposeconv3).forward4(_9, )
-  def forward5(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
-    input: Tensor) -> Tensor:
-    transposeconv3 = self.transposeconv3
-    act2 = self.act2
-    transposeconv2 = self.transposeconv2
-    act1 = self.act1
-    transposeconv1 = self.transposeconv1
-    _10 = (act1).forward5((transposeconv1).forward5(input, ), )
-    _11 = (act2).forward5((transposeconv2).forward5(_10, ), )
-    return (transposeconv3).forward5(_11, )
-  def forward6(self: __torch__.torch.nn.modules.container.___torch_mangle_527.Sequential,
-    input: Tensor) -> Tensor:
-    transposeconv3 = self.transposeconv3
-    act2 = self.act2
-    transposeconv2 = self.transposeconv2
-    act1 = self.act1
-    transposeconv1 = self.transposeconv1
-    _12 = (act1).forward6((transposeconv1).forward6(input, ), )
-    _13 = (act2).forward6((transposeconv2).forward6(_12, ), )
-    return (transposeconv3).forward6(_13, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_527.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_527.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_527.py.debug_pkl
+++ /dev/null
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_540.py
+++ /dev/null
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-class Sequential(Module):
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+++ /dev/null
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-class Sequential(Module):
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+++ /dev/null
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_595.py
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-  _is_full_backward_hook : Optional[bool]
-  __annotations__["0"] = __torch__.dblock.___torch_mangle_965.GroupConv2d
-  __annotations__["1"] = __torch__.dblock.___torch_mangle_969.GroupConv2d
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-  __annotations__["3"] = __torch__.dblock.___torch_mangle_977.GroupConv2d
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_978.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_978.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_978.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_997.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_997.py
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_997.py
+++ /dev/null
@@ -1,9 +0,0 @@
-class Sequential(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  __annotations__["0"] = __torch__.dblock.___torch_mangle_984.GroupConv2d
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/container/___torch_mangle_999.py
+++ /dev/null
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-class Sequential(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  __annotations__["0"] = __torch__.dblock.___torch_mangle_865.Inception
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv.py
+++ /dev/null
@@ -1,75 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input0 = torch._convolution(input, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input0
-class ConvTranspose2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input1 = torch._convolution(input, weight, bias, [2, 2], [1, 1], [1, 1], True, [1, 1], 1, False, False, True, True)
-    return input1
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_0.py
+++ /dev/null
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-class Conv2d(Module):
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-  __buffers__ = []
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-  bias : Tensor
-  training : bool
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [2, 2], [1, 1], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_10.py
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-class Conv2d(Module):
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-  __buffers__ = []
-  weight : Tensor
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [2, 2], [1, 1], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input
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+++ /dev/null
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-class ConvTranspose2d(Module):
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-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_1000.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    input0 = torch._convolution(input, weight, bias, [2, 2], [1, 1], [1, 1], True, [1, 1], 1, False, False, True, True)
-    return input0
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-    return input0
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-    return _0
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-class Conv2d(Module):
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-    return input
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-    return input
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_109.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_109.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_113.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_113.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [2, 2], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_113.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_113.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_117.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
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-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [3, 3], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_117.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_117.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_121.py
+++ /dev/null
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_121.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_121.py.debug_pkl
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_127.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_127.py.debug_pkl
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_128.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_128.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_132.py
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_132.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_132.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_136.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_136.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_136.py.debug_pkl
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_140.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_140.py.debug_pkl
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_146.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_146.py.debug_pkl
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-class Conv2d(Module):
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_147.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_147.py.debug_pkl
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-    return input0
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_15.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_15.py.debug_pkl
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_151.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_151.py.debug_pkl
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index e57d9a04da33c2aaff2ab11efc46d52b5cf4482a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_155.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_155.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [3, 3], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_155.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_155.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_159.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
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-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_159.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [5, 5], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_159.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_159.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_16.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-  __buffers__ = []
-  weight : Tensor
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-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_16.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_16.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_16.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_166.py
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-class Conv2d(Module):
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_166.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_166.py.debug_pkl
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+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_167.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_167.py.debug_pkl
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+++ /dev/null
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_171.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_171.py.debug_pkl
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+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_175.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_175.py.debug_pkl
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+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_179.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_179.py.debug_pkl
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-class Conv2d(Module):
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-    return input0
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_185.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_185.py.debug_pkl
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_186.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_186.py.debug_pkl
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_19.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_19.py.debug_pkl
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_190.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_190.py.debug_pkl
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [3, 3], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_194.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_194.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_198.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_198.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [5, 5], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_198.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_198.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_204.py
+++ /dev/null
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-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
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-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return input0
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_204.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_204.py.debug_pkl
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_205.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_205.py.debug_pkl
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-    return input
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_213.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_213.py.debug_pkl
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_217.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_217.py.debug_pkl
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+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
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-    return input0
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_223.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_223.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_224.py
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_224.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_224.py.debug_pkl
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_228.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_228.py.debug_pkl
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_23.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_23.py.debug_pkl
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_232.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_232.py.debug_pkl
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-    return input
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_242.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_242.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    input0 = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input0
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_242.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_242.py.debug_pkl
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+++ /dev/null
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-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_243.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_243.py.debug_pkl
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+++ /dev/null
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-class Conv2d(Module):
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-    return input
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-class Conv2d(Module):
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-    return input
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-class Conv2d(Module):
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-    return input
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-class Conv2d(Module):
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-    return input0
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-class Conv2d(Module):
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-    return input
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-class Conv2d(Module):
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-    return input
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_27.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_27.py.debug_pkl
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-    return input
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_281.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_281.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_285.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
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-  weight : Tensor
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-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_285.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [2, 2], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_285.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_285.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_289.py
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-class Conv2d(Module):
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-    argument_1: Tensor) -> Tensor:
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_289.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_289.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_293.py
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_293.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_293.py.debug_pkl
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-class Conv2d(Module):
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-    return input0
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_300.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_300.py.debug_pkl
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-class Conv2d(Module):
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-    return input
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+++ /dev/null
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-class Conv2d(Module):
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-    return input
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_312.py
+++ /dev/null
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-class Conv2d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_312.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_312.py.debug_pkl
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-    return input
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_323.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_323.py.debug_pkl
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+++ /dev/null
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-class ConvTranspose2d(Module):
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-    return input
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_33.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_33.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_333.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_333.py
deleted file mode 100644
index 3397dfc9518ab352ce1047aa3f1ddcb79e20869d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_333.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_333.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input0 = torch._convolution(input, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input0
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_333.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_333.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_333.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_339.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_339.py
deleted file mode 100644
index e9f80d2810aaef3fa665494e87504f682544ff22..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_339.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_339.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    Y = torch._convolution(argument_1, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return Y
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_339.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_339.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_339.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_340.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_340.py
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index a33ff4e3ea4992b5a7dc6c7e109f757d3d6b169c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_340.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class ConvTranspose2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_340.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_340.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_340.py.debug_pkl
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index 81ae492094561799e18e8520f4c6c04227705d78..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_341.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_341.Conv2d,
-    x: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(x, weight, bias, [16, 16], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_341.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_341.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_341.py.debug_pkl
+++ /dev/null
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_348.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_348.py
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index 95d47943eb2d6fc3727cea447e2ed25fec506ceb..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_348.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_348.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_348.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_348.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_364.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_364.py
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index 6471975f59c6c466ba59c55d4d2ca40bacc6a800..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_364.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_364.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_364.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_364.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_37.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_37.py
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_37.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_37.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [2, 2], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_37.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_37.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_380.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_380.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_380.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_380.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_396.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_396.py
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index adb17b3fa270b608ce0b9cd8dbe683faee0d41b2..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_396.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_396.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_396.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_396.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_41.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_41.py
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index c021130ffc5a2e76fd3fbc34df0eb0493639e539..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_41.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_41.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [3, 3], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_41.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_41.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_412.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_412.py
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index caff6835bf4cbb1932820596d2c0547d4fa7e47c..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_412.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_412.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_412.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_412.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_428.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_428.py
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index d9818c712fb775fefc62a06b33020e931fcd9bae..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_428.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
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-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_428.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_428.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_428.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_444.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_444.py
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index 8994117ee936358fd2f27a2aa7d5116c9514e783..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_444.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_444.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_444.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_444.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_45.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_45.py
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_45.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_45.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [5, 5], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_45.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_45.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_460.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_460.py
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_460.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
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-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_460.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_460.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_460.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_476.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_476.py
deleted file mode 100644
index 681245c6bd2367174242fb17ee725ece4df69997..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_476.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_476.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_476.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_476.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_492.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_492.py
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index 3cbac124402a53a5ff424d404104d64df1d32b8d..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_492.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_492.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_492.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_492.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_5.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_5.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_5.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_5.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_508.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_508.py
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index 344d38f9c1dab40665d5a5967e59b089e88b3973..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_508.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Conv1d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _0 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _1 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _2 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _4 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _5 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_508.Conv1d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _6 = torch._convolution(input, weight, bias, [1], [0], [1], False, [0], 1, False, False, True, True)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_508.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_508.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_51.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_51.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_51.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_51.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_52.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_52.py
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_52.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_52.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_52.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_52.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_523.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class ConvTranspose2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input0 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input1 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input2 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input3 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input4 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input5 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input5
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_523.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input6 = torch._convolution(input, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_523.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_523.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_524.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class ConvTranspose2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
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-    return input
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_524.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [2, 2], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_524.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_524.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_524.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_526.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_526.py
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_526.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class ConvTranspose2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(argument_1, weight, bias, [4, 4], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(argument_1, weight, bias, [4, 4], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(argument_1, weight, bias, [4, 4], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(argument_1, weight, bias, [4, 4], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(argument_1, weight, bias, [4, 4], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(argument_1, weight, bias, [4, 4], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
-  def forward6(self: __torch__.torch.nn.modules.conv.___torch_mangle_526.ConvTranspose2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch._convolution(argument_1, weight, bias, [4, 4], [0, 0], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return x
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_526.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_526.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_529.py
+++ /dev/null
@@ -1,25 +0,0 @@
-class ConvTranspose2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
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-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
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-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return _0
-  def forward1(self: __torch__.torch.nn.modules.conv.___torch_mangle_529.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_529.ConvTranspose2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    _2 = torch._convolution(input, weight, bias, [1, 1], [1, 1], [1, 1], True, [0, 0], 1, False, False, True, True)
-    return _2
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_529.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_529.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_530.py
+++ /dev/null
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-    return _0
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-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_530.Conv2d,
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-    return _2
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_531.py
+++ /dev/null
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-class Conv2d(Module):
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-  __buffers__ = []
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-  bias : Tensor
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-  _is_full_backward_hook : Optional[bool]
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-    bias = self.bias
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-    return _0
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-    input: Tensor) -> Tensor:
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-    return _1
-  def forward2(self: __torch__.torch.nn.modules.conv.___torch_mangle_531.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return _2
-  def forward3(self: __torch__.torch.nn.modules.conv.___torch_mangle_531.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    _3 = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _3
-  def forward4(self: __torch__.torch.nn.modules.conv.___torch_mangle_531.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return _4
-  def forward5(self: __torch__.torch.nn.modules.conv.___torch_mangle_531.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    _5 = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return _5
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_531.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_531.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_532.py
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-class Conv2d(Module):
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-    return input0
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_532.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_532.py.debug_pkl
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-class Conv2d(Module):
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-    return input
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-    return input0
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-    return input
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [2, 2], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_546.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_546.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_550.py
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-class Conv2d(Module):
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-    return input
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-    return input
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-class Conv2d(Module):
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-    return input
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-class Conv2d(Module):
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-    return input
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-class Conv2d(Module):
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-    return input
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-class Conv2d(Module):
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-    return input
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-    return input
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_635.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_635.py.debug_pkl
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-    return input
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index 1e8617b4d622c150ce31813f24c7789cd2a39622..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_678.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_678.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [2, 2], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_678.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_678.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_682.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_682.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [3, 3], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_682.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_682.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_686.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_686.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [5, 5], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_686.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_686.py.debug_pkl
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-class Conv1d(Module):
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-  _is_full_backward_hook : Optional[bool]
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-    input: Tensor) -> Tensor:
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-    return _0
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+++ /dev/null
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-class Conv2d(Module):
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
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-class Conv1d(Module):
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-class Conv2d(Module):
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-    return input
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-class Conv1d(Module):
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-class Conv1d(Module):
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-    return _0
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-    return input
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-    return _0
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-class Conv1d(Module):
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-    return _0
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-    return input
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-    return input
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-    return input
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_866.py
+++ /dev/null
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-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
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-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_866.Conv2d,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    input0 = torch._convolution(input, weight, bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
-    return input0
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_866.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_866.py.debug_pkl
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+++ /dev/null
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-class Conv2d(Module):
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-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_867.Conv2d,
-    argument_1: Tensor) -> Tensor:
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_867.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_867.py.debug_pkl
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-class Conv2d(Module):
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-    return input
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-    return input
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-    return input
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+++ /dev/null
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-class Conv2d(Module):
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-    return input
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-    return input
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_947.py
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-class Conv2d(Module):
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [2, 2], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
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-class Conv2d(Module):
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-    return input
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-    input = torch._convolution(argument_1, weight, bias, [1, 1], [3, 3], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_989.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_989.py.debug_pkl
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index 22870e3afc22e8224a4fd93502eb5ab0d97d8789..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_993.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class Conv2d(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_993.Conv2d,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch._convolution(argument_1, weight, bias, [1, 1], [5, 5], [1, 1], False, [0, 0], 8, False, False, True, True)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_993.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/conv/___torch_mangle_993.py.debug_pkl
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index aa9b38aa7a0bd39de76331cc9396130a21e8c440..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout.py
+++ /dev/null
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-class Dropout(Module):
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-  def forward(self: __torch__.torch.nn.modules.dropout.Dropout,
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-    return torch.dropout(input, 0., False)
-  def forward1(self: __torch__.torch.nn.modules.dropout.Dropout,
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-    return torch.dropout(input, 0., False)
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-    input: Tensor) -> Tensor:
-    return torch.dropout(input, 0., False)
-  def forward3(self: __torch__.torch.nn.modules.dropout.Dropout,
-    input: Tensor) -> Tensor:
-    return torch.dropout(input, 0., False)
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-    input: Tensor) -> Tensor:
-    return torch.dropout(input, 0., False)
-  def forward5(self: __torch__.torch.nn.modules.dropout.Dropout,
-    input: Tensor) -> Tensor:
-    return torch.dropout(input, 0., False)
-  def forward6(self: __torch__.torch.nn.modules.dropout.Dropout,
-    input: Tensor) -> Tensor:
-    return torch.dropout(input, 0., False)
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_345.py
+++ /dev/null
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-class Dropout(Module):
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
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-    return _0
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-    return input
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-    return _1
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-    return input
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-    return _2
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-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
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-    argument_1: Tensor) -> Tensor:
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-    return _3
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-    return input
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-    return _5
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-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward13(self: __torch__.torch.nn.modules.dropout.___torch_mangle_345.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _6 = torch.dropout(argument_1, 0., False)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_345.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_345.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_359.py
+++ /dev/null
@@ -1,61 +0,0 @@
-class Dropout(Module):
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
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-    return input
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-    return _0
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-    return input
-  def forward3(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _1 = torch.dropout(argument_1, 0., False)
-    return _1
-  def forward4(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
-    argument_1: Tensor) -> Tensor:
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-    return input
-  def forward5(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
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-    _2 = torch.dropout(argument_1, 0., False)
-    return _2
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-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
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-    argument_1: Tensor) -> Tensor:
-    _3 = torch.dropout(argument_1, 0., False)
-    return _3
-  def forward8(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward9(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _4 = torch.dropout(argument_1, 0., False)
-    return _4
-  def forward10(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
-    argument_1: Tensor) -> Tensor:
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-    return input
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-    return _5
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-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward13(self: __torch__.torch.nn.modules.dropout.___torch_mangle_359.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _6 = torch.dropout(argument_1, 0., False)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_359.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_359.py.debug_pkl
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+++ /dev/null
@@ -1,61 +0,0 @@
-class Dropout(Module):
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-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
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-    return input
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-    return input
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-    return _1
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-    return _2
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-    return input
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-    return _3
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-    return input
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-    _4 = torch.dropout(argument_1, 0., False)
-    return _4
-  def forward10(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
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-    return input
-  def forward11(self: __torch__.torch.nn.modules.dropout.___torch_mangle_375.Dropout,
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_375.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_375.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_391.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_391.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_407.py
+++ /dev/null
@@ -1,61 +0,0 @@
-class Dropout(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward1(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.dropout(argument_1, 0., False)
-    return _0
-  def forward2(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _1 = torch.dropout(argument_1, 0., False)
-    return _1
-  def forward4(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _2 = torch.dropout(argument_1, 0., False)
-    return _2
-  def forward6(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward7(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _3 = torch.dropout(argument_1, 0., False)
-    return _3
-  def forward8(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward9(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _4 = torch.dropout(argument_1, 0., False)
-    return _4
-  def forward10(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward11(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _5 = torch.dropout(argument_1, 0., False)
-    return _5
-  def forward12(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward13(self: __torch__.torch.nn.modules.dropout.___torch_mangle_407.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _6 = torch.dropout(argument_1, 0., False)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_407.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_407.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_423.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_423.py
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index edaed607b08b91a7c0999a8e51e6924a3756e155..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_423.py
+++ /dev/null
@@ -1,61 +0,0 @@
-class Dropout(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward1(self: __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _0 = torch.dropout(argument_1, 0., False)
-    return _0
-  def forward2(self: __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _1 = torch.dropout(argument_1, 0., False)
-    return _1
-  def forward4(self: __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _2 = torch.dropout(argument_1, 0., False)
-    return _2
-  def forward6(self: __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward7(self: __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _3 = torch.dropout(argument_1, 0., False)
-    return _3
-  def forward8(self: __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward9(self: __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _4 = torch.dropout(argument_1, 0., False)
-    return _4
-  def forward10(self: __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward11(self: __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _5 = torch.dropout(argument_1, 0., False)
-    return _5
-  def forward12(self: __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward13(self: __torch__.torch.nn.modules.dropout.___torch_mangle_423.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _6 = torch.dropout(argument_1, 0., False)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_423.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_423.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_439.py
+++ /dev/null
@@ -1,61 +0,0 @@
-class Dropout(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
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-    _0 = torch.dropout(argument_1, 0., False)
-    return _0
-  def forward2(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _1 = torch.dropout(argument_1, 0., False)
-    return _1
-  def forward4(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _2 = torch.dropout(argument_1, 0., False)
-    return _2
-  def forward6(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward7(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _3 = torch.dropout(argument_1, 0., False)
-    return _3
-  def forward8(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward9(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _4 = torch.dropout(argument_1, 0., False)
-    return _4
-  def forward10(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward11(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _5 = torch.dropout(argument_1, 0., False)
-    return _5
-  def forward12(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward13(self: __torch__.torch.nn.modules.dropout.___torch_mangle_439.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _6 = torch.dropout(argument_1, 0., False)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_439.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_439.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_455.py
+++ /dev/null
@@ -1,61 +0,0 @@
-class Dropout(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
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-    input = torch.dropout(argument_1, 0., False)
-    return input
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-    return _0
-  def forward2(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _1 = torch.dropout(argument_1, 0., False)
-    return _1
-  def forward4(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
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-    return input
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-    argument_1: Tensor) -> Tensor:
-    _2 = torch.dropout(argument_1, 0., False)
-    return _2
-  def forward6(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward7(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _3 = torch.dropout(argument_1, 0., False)
-    return _3
-  def forward8(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
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-    input = torch.dropout(argument_1, 0., False)
-    return input
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-    argument_1: Tensor) -> Tensor:
-    _4 = torch.dropout(argument_1, 0., False)
-    return _4
-  def forward10(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward11(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
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-    _5 = torch.dropout(argument_1, 0., False)
-    return _5
-  def forward12(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
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-    input = torch.dropout(argument_1, 0., False)
-    return input
-  def forward13(self: __torch__.torch.nn.modules.dropout.___torch_mangle_455.Dropout,
-    argument_1: Tensor) -> Tensor:
-    _6 = torch.dropout(argument_1, 0., False)
-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_455.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_455.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_471.py
+++ /dev/null
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-  def forward(self: __torch__.torch.nn.modules.dropout.___torch_mangle_471.Dropout,
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-    return _1
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-    return _2
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-    return input
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-    return _3
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-    return input
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-    return _4
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-    return input
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-    return _6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_471.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/dropout/___torch_mangle_471.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_351.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_351.py
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_351.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class NonDynamicallyQuantizableLinear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_351.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_351.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_355.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_355.py
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_355.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_355.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
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-    return input
-  def forward1(self: __torch__.torch.nn.modules.linear.___torch_mangle_355.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward2(self: __torch__.torch.nn.modules.linear.___torch_mangle_355.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_355.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_355.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_355.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_355.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_355.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_355.py.debug_pkl
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-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_357.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_357.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_366.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class Identity(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_366.Identity) -> NoneType:
-    return None
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-    return None
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-    return None
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-    return None
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_366.Identity) -> NoneType:
-    return None
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_366.Identity) -> NoneType:
-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_366.Identity) -> NoneType:
-    return None
-  def forward7(self: __torch__.torch.nn.modules.linear.___torch_mangle_366.Identity) -> NoneType:
-    return None
-  def forward8(self: __torch__.torch.nn.modules.linear.___torch_mangle_366.Identity) -> NoneType:
-    return None
-  def forward9(self: __torch__.torch.nn.modules.linear.___torch_mangle_366.Identity) -> NoneType:
-    return None
-  def forward10(self: __torch__.torch.nn.modules.linear.___torch_mangle_366.Identity) -> NoneType:
-    return None
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-    return None
-  def forward12(self: __torch__.torch.nn.modules.linear.___torch_mangle_366.Identity) -> NoneType:
-    return None
-  def forward13(self: __torch__.torch.nn.modules.linear.___torch_mangle_366.Identity) -> NoneType:
-    return None
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_366.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_366.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_367.py
+++ /dev/null
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-class NonDynamicallyQuantizableLinear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_367.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_367.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_371.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_371.py
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_371.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_371.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
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-    return input
-  def forward1(self: __torch__.torch.nn.modules.linear.___torch_mangle_371.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward2(self: __torch__.torch.nn.modules.linear.___torch_mangle_371.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_371.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_371.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_371.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_371.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_371.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_371.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_373.py
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@@ -1,7 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_373.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_373.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_382.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_382.py
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_382.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class Identity(Module):
-  __parameters__ = []
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_382.Identity) -> NoneType:
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-    return None
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_382.Identity) -> NoneType:
-    return None
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_382.Identity) -> NoneType:
-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_382.Identity) -> NoneType:
-    return None
-  def forward7(self: __torch__.torch.nn.modules.linear.___torch_mangle_382.Identity) -> NoneType:
-    return None
-  def forward8(self: __torch__.torch.nn.modules.linear.___torch_mangle_382.Identity) -> NoneType:
-    return None
-  def forward9(self: __torch__.torch.nn.modules.linear.___torch_mangle_382.Identity) -> NoneType:
-    return None
-  def forward10(self: __torch__.torch.nn.modules.linear.___torch_mangle_382.Identity) -> NoneType:
-    return None
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-    return None
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-    return None
-  def forward13(self: __torch__.torch.nn.modules.linear.___torch_mangle_382.Identity) -> NoneType:
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_382.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_382.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_383.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class NonDynamicallyQuantizableLinear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_383.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_383.py.debug_pkl
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-version https://git-lfs.github.com/spec/v1
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_387.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_387.py
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_387.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_387.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward1(self: __torch__.torch.nn.modules.linear.___torch_mangle_387.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward2(self: __torch__.torch.nn.modules.linear.___torch_mangle_387.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_387.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_387.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_387.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_387.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_387.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_387.py.debug_pkl
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+++ /dev/null
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_389.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_389.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_389.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_398.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_398.py
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index f2845987b2e8faa96a7710ab1612e01422ce1a9a..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_398.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class Identity(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
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-    return None
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward7(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward8(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
-  def forward9(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
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-  def forward13(self: __torch__.torch.nn.modules.linear.___torch_mangle_398.Identity) -> NoneType:
-    return None
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_399.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class NonDynamicallyQuantizableLinear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_403.py
+++ /dev/null
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-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
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-    argument_1: Tensor) -> Tensor:
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-    return input
-  def forward1(self: __torch__.torch.nn.modules.linear.___torch_mangle_403.Linear,
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-    return input
-  def forward2(self: __torch__.torch.nn.modules.linear.___torch_mangle_403.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_403.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_403.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_403.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_403.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_403.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_403.py.debug_pkl
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-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  _is_full_backward_hook : Optional[bool]
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_414.py
+++ /dev/null
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-class Identity(Module):
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-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
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-    return None
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward7(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward8(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
-  def forward9(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
-    return None
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-  def forward13(self: __torch__.torch.nn.modules.linear.___torch_mangle_414.Identity) -> NoneType:
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_414.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_414.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_415.py
+++ /dev/null
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-class NonDynamicallyQuantizableLinear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_419.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_419.py
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_419.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  training : bool
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-    return input
-  def forward2(self: __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear,
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-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear,
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-    bias = self.bias
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-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_419.Linear,
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-    bias = self.bias
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-    return input
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-    return input
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-    bias = self.bias
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_419.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_419.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_430.py
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-class Identity(Module):
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-    return None
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-    return None
-  def forward9(self: __torch__.torch.nn.modules.linear.___torch_mangle_430.Identity) -> NoneType:
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-    return None
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_430.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_430.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_431.py
+++ /dev/null
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-class NonDynamicallyQuantizableLinear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  training : bool
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_431.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_431.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_435.py
+++ /dev/null
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-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_435.Linear,
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-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_435.Linear,
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-    bias = self.bias
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-    return input
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-    return input
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-    argument_1: Tensor) -> Tensor:
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_435.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_435.py.debug_pkl
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-class Linear(Module):
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_446.py
+++ /dev/null
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-class Identity(Module):
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-    return None
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-    return None
-  def forward7(self: __torch__.torch.nn.modules.linear.___torch_mangle_446.Identity) -> NoneType:
-    return None
-  def forward8(self: __torch__.torch.nn.modules.linear.___torch_mangle_446.Identity) -> NoneType:
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-class NonDynamicallyQuantizableLinear(Module):
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_451.py
+++ /dev/null
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-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
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-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_451.Linear,
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-    bias = self.bias
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-    return input
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-    return input
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-    argument_1: Tensor) -> Tensor:
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_451.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_451.py.debug_pkl
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-class NonDynamicallyQuantizableLinear(Module):
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-class Linear(Module):
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-  def forward3(self: __torch__.torch.nn.modules.linear.___torch_mangle_467.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_467.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward5(self: __torch__.torch.nn.modules.linear.___torch_mangle_467.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_467.Linear,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.linear(argument_1, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_467.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_467.py.debug_pkl
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+++ /dev/null
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_469.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_478.py
+++ /dev/null
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-class Identity(Module):
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-  def forward(self: __torch__.torch.nn.modules.linear.___torch_mangle_478.Identity) -> NoneType:
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-    return None
-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_478.Identity) -> NoneType:
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-class NonDynamicallyQuantizableLinear(Module):
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_483.py
+++ /dev/null
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-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
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-  weight : Tensor
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-    return input
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-    bias = self.bias
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-    return input
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_483.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_483.py.debug_pkl
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-class Linear(Module):
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-class NonDynamicallyQuantizableLinear(Module):
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-class Linear(Module):
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_499.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_499.py.debug_pkl
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-  def forward6(self: __torch__.torch.nn.modules.linear.___torch_mangle_510.Identity) -> NoneType:
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-  def forward13(self: __torch__.torch.nn.modules.linear.___torch_mangle_510.Identity) -> NoneType:
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_510.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_510.py.debug_pkl
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-class NonDynamicallyQuantizableLinear(Module):
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-  __buffers__ = []
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/linear/___torch_mangle_515.py
+++ /dev/null
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-class Linear(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
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-  _is_full_backward_hook : Optional[bool]
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-    bias = self.bias
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-    return input
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-  def forward4(self: __torch__.torch.nn.modules.linear.___torch_mangle_515.Linear,
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-    return input
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization.py
+++ /dev/null
@@ -1,62 +0,0 @@
-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.GroupNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
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-    return input
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
-  def forward1(self: __torch__.torch.nn.modules.normalization.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return x
-  def forward2(self: __torch__.torch.nn.modules.normalization.LayerNorm,
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-    return x
-  def forward3(self: __torch__.torch.nn.modules.normalization.LayerNorm,
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-    return x
-  def forward4(self: __torch__.torch.nn.modules.normalization.LayerNorm,
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-    return x
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return x
-  def forward6(self: __torch__.torch.nn.modules.normalization.LayerNorm,
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-    bias = self.bias
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-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization.py.debug_pkl
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-class GroupNorm(Module):
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-    return input
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-    return input
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-class GroupNorm(Module):
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_191.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_191.py.debug_pkl
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-class GroupNorm(Module):
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_195.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_195.py.debug_pkl
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-class GroupNorm(Module):
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_199.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_199.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_244.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_244.py.debug_pkl
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_305.py
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_305.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_305.py.debug_pkl
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_34.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_34.py.debug_pkl
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-class LayerNorm(Module):
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-    return input2
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-    return input3
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_343.LayerNorm,
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_343.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_343.py.debug_pkl
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-class LayerNorm(Module):
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-    return x
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-    return input0
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    input1 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input1
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return input2
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input3 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input3
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return input4
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    input5 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input5
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_354.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    input6 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_354.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_354.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_362.py
+++ /dev/null
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-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
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-    return x
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return x
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return x
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return x
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
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-    bias = self.bias
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-    return x
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
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-    bias = self.bias
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-    return x
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_362.LayerNorm,
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-    bias = self.bias
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-    return x
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_362.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_362.py.debug_pkl
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-class LayerNorm(Module):
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-    return input1
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-    return input2
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-    return input5
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-    bias = self.bias
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-    input6 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_370.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_370.py.debug_pkl
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-    return x
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-    return x
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-    return x
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-    return x
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-    bias = self.bias
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-    return x
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_378.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_378.py.debug_pkl
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-class GroupNorm(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_38.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_38.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_386.py
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-class LayerNorm(Module):
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-    return input2
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-    return input6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_386.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_386.py.debug_pkl
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-class LayerNorm(Module):
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-    return x
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_394.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_394.py.debug_pkl
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-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
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-    return input0
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return input1
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return input2
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input3 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input3
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input4 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input4
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input5 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input5
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_402.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    input6 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_402.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_402.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_410.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
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-    return x
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return x
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return x
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return x
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
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-    bias = self.bias
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-    return x
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-    return x
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_410.LayerNorm,
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-    bias = self.bias
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-    x = torch.layer_norm(argument_1, [768], weight, bias, 9.9999999999999995e-07)
-    return x
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_410.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_410.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_418.py
+++ /dev/null
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-class LayerNorm(Module):
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-  training : bool
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-    return input0
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-    return input1
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-    return input2
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-    return input3
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-    return input5
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-    bias = self.bias
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-    return input6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_418.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_418.py.debug_pkl
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-class GroupNorm(Module):
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-  bias : Tensor
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_42.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_42.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_426.py
+++ /dev/null
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-class LayerNorm(Module):
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-    return x
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-    return x
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_426.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_426.py.debug_pkl
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-class LayerNorm(Module):
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-    return input6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_434.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_434.py.debug_pkl
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-class LayerNorm(Module):
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_442.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_442.py.debug_pkl
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_450.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
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-    return input0
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_450.LayerNorm,
-    input: Tensor) -> Tensor:
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-    return input1
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_450.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return input2
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_450.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    input3 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input3
-  def forward4(self: __torch__.torch.nn.modules.normalization.___torch_mangle_450.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    input4 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input4
-  def forward5(self: __torch__.torch.nn.modules.normalization.___torch_mangle_450.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    input5 = torch.layer_norm(input, [768], weight, bias, 9.9999999999999995e-07)
-    return input5
-  def forward6(self: __torch__.torch.nn.modules.normalization.___torch_mangle_450.LayerNorm,
-    input: Tensor) -> Tensor:
-    bias = self.bias
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-    return input6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_450.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_450.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_458.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_458.py
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_458.py
+++ /dev/null
@@ -1,49 +0,0 @@
-class LayerNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
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-    return x
-  def forward1(self: __torch__.torch.nn.modules.normalization.___torch_mangle_458.LayerNorm,
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-    return x
-  def forward2(self: __torch__.torch.nn.modules.normalization.___torch_mangle_458.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return x
-  def forward3(self: __torch__.torch.nn.modules.normalization.___torch_mangle_458.LayerNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    return x
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-    bias = self.bias
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-    return x
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-    return x
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-    bias = self.bias
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-    return x
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
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-  weight : Tensor
-  bias : Tensor
-  training : bool
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-    return input
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_466.py
+++ /dev/null
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-class LayerNorm(Module):
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-    return input2
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-    return input6
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_466.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_466.py.debug_pkl
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-class LayerNorm(Module):
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-    return x
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_474.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_474.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_482.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_482.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_490.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_490.py.debug_pkl
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-  __parameters__ = ["weight", "bias", ]
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-    return input0
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_506.py
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-class LayerNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-    return input
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-class GroupNorm(Module):
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-class LayerNorm(Module):
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-    return input0
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-class GroupNorm(Module):
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_857.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_857.py.debug_pkl
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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-class GroupNorm(Module):
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_971.py
+++ /dev/null
@@ -1,13 +0,0 @@
-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_971.GroupNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_971.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_971.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_971.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_975.py
+++ /dev/null
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_975.GroupNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_975.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_975.py.debug_pkl
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_982.GroupNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
-    weight = self.weight
-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_982.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_982.py.debug_pkl
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-class GroupNorm(Module):
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-    argument_1: Tensor) -> Tensor:
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_986.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_986.py.debug_pkl
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-class GroupNorm(Module):
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-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
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-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_99.GroupNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_99.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_99.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_990.py
+++ /dev/null
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-class GroupNorm(Module):
-  __parameters__ = ["weight", "bias", ]
-  __buffers__ = []
-  weight : Tensor
-  bias : Tensor
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-  def forward(self: __torch__.torch.nn.modules.normalization.___torch_mangle_990.GroupNorm,
-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_990.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_990.py.debug_pkl
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-class GroupNorm(Module):
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-    argument_1: Tensor) -> Tensor:
-    bias = self.bias
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-    input = torch.group_norm(argument_1, 8, weight, bias)
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_994.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/normalization/___torch_mangle_994.py.debug_pkl
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index a68199c770434ef1157a86d8221b625ee77f4d04..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class AdaptiveAvgPool1d(Module):
-  __parameters__ = []
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward1(self: __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward2(self: __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward4(self: __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling.py.debug_pkl
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+++ /dev/null
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-class AdaptiveAvgPool1d(Module):
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-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
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-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_358.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_358.py.debug_pkl
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-class AdaptiveAvgPool1d(Module):
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-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_374.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_374.py.debug_pkl
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-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward2(self: __torch__.torch.nn.modules.pooling.___torch_mangle_390.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.___torch_mangle_390.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward4(self: __torch__.torch.nn.modules.pooling.___torch_mangle_390.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_390.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.___torch_mangle_390.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_390.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_390.py.debug_pkl
deleted file mode 100644
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_390.py.debug_pkl
+++ /dev/null
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_406.py b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_406.py
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index 64d03ecffeabd15fab1900147f55669a00fc706f..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_406.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class AdaptiveAvgPool1d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward1(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward2(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward4(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.___torch_mangle_406.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_406.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_406.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_422.py
+++ /dev/null
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-class AdaptiveAvgPool1d(Module):
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
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-    return input
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-    return input
-  def forward2(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward4(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.___torch_mangle_422.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_422.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_422.py.debug_pkl
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index 74235093b1738a7daf37c33e3050fc087ffa27b9..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_438.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class AdaptiveAvgPool1d(Module):
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_438.AdaptiveAvgPool1d,
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-    return input
-  def forward2(self: __torch__.torch.nn.modules.pooling.___torch_mangle_438.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.___torch_mangle_438.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward4(self: __torch__.torch.nn.modules.pooling.___torch_mangle_438.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_438.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.___torch_mangle_438.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_438.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_438.py.debug_pkl
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index 009c360ec1c2ba95611ebb22d668593524ff863e..0000000000000000000000000000000000000000
--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_454.py
+++ /dev/null
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-class AdaptiveAvgPool1d(Module):
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-  training : bool
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-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_454.AdaptiveAvgPool1d,
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-    return input
-  def forward2(self: __torch__.torch.nn.modules.pooling.___torch_mangle_454.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.___torch_mangle_454.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward4(self: __torch__.torch.nn.modules.pooling.___torch_mangle_454.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_454.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.___torch_mangle_454.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_454.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_454.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_470.py
+++ /dev/null
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-class AdaptiveAvgPool1d(Module):
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-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_470.AdaptiveAvgPool1d,
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-    return input
-  def forward3(self: __torch__.torch.nn.modules.pooling.___torch_mangle_470.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
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-    return input
-  def forward4(self: __torch__.torch.nn.modules.pooling.___torch_mangle_470.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_470.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.___torch_mangle_470.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_470.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_470.py.debug_pkl
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--- a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_486.py
+++ /dev/null
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-class AdaptiveAvgPool1d(Module):
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-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_486.AdaptiveAvgPool1d,
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-  def forward4(self: __torch__.torch.nn.modules.pooling.___torch_mangle_486.AdaptiveAvgPool1d,
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-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_486.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
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-    return input
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diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_486.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_486.py.debug_pkl
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-class AdaptiveAvgPool1d(Module):
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-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_502.AdaptiveAvgPool1d,
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-  def forward2(self: __torch__.torch.nn.modules.pooling.___torch_mangle_502.AdaptiveAvgPool1d,
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-  def forward3(self: __torch__.torch.nn.modules.pooling.___torch_mangle_502.AdaptiveAvgPool1d,
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-  def forward4(self: __torch__.torch.nn.modules.pooling.___torch_mangle_502.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward5(self: __torch__.torch.nn.modules.pooling.___torch_mangle_502.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [768])
-    return input
-  def forward6(self: __torch__.torch.nn.modules.pooling.___torch_mangle_502.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
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-class AdaptiveAvgPool1d(Module):
-  __parameters__ = []
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-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  def forward(self: __torch__.torch.nn.modules.pooling.___torch_mangle_842.AdaptiveAvgPool1d,
-    argument_1: Tensor) -> Tensor:
-    input = torch.adaptive_avg_pool1d(argument_1, [128])
-    return input
diff --git a/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_842.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/torch/nn/modules/pooling/___torch_mangle_842.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/utils.py b/xe_weights14/glonet_p3/code/__torch__/utils.py
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--- a/xe_weights14/glonet_p3/code/__torch__/utils.py
+++ /dev/null
@@ -1,57 +0,0 @@
-class Encoder(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  enc : __torch__.torch.nn.modules.container.___torch_mangle_540.Sequential
-  def forward(self: __torch__.utils.Encoder,
-    input: Tensor) -> Tuple[Tensor, Tensor]:
-    enc = self.enc
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-    return ((_1).forward(_2, ), _2)
-class ConvSC(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.utils.BasicConv2d
-  def forward(self: __torch__.utils.ConvSC,
-    input: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(input, )
-class BasicConv2d(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_532.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_533.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_534.LeakyReLU
-  def forward(self: __torch__.utils.BasicConv2d,
-    input: Tensor) -> Tensor:
-    act = self.act
-    norm = self.norm
-    conv = self.conv
-    _1 = (norm).forward((conv).forward(input, ), )
-    return (act).forward(_1, )
-class Decoder(Module):
-  __parameters__ = []
-  __buffers__ = []
-  training : bool
-  _is_full_backward_hook : Optional[bool]
-  dec : __torch__.torch.nn.modules.container.___torch_mangle_1010.Sequential
-  readout : __torch__.torch.nn.modules.conv.___torch_mangle_1011.Conv2d
-  def forward(self: __torch__.utils.Decoder,
-    input: Tensor,
-    enc1: Tensor) -> Tensor:
-    readout = self.readout
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-    _0 = getattr(dec0, "0")
-    input0 = torch.cat([(_0).forward(input, ), enc1], 1)
-    _2 = (readout).forward((_1).forward(input0, ), )
-    return _2
diff --git a/xe_weights14/glonet_p3/code/__torch__/utils.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/utils.py.debug_pkl
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diff --git a/xe_weights14/glonet_p3/code/__torch__/utils/___torch_mangle_1003.py b/xe_weights14/glonet_p3/code/__torch__/utils/___torch_mangle_1003.py
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+++ /dev/null
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-class BasicConv2d(Module):
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-  act : __torch__.torch.nn.modules.activation.___torch_mangle_1002.LeakyReLU
-  def forward(self: __torch__.utils.___torch_mangle_1003.BasicConv2d,
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-    _0 = (norm).forward((conv).forward(input, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/utils/___torch_mangle_1003.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/utils/___torch_mangle_1003.py.debug_pkl
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-class ConvSC(Module):
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-  conv : __torch__.utils.___torch_mangle_1003.BasicConv2d
-  def forward(self: __torch__.utils.___torch_mangle_1004.ConvSC,
-    input: Tensor) -> Tensor:
-    conv = self.conv
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diff --git a/xe_weights14/glonet_p3/code/__torch__/utils/___torch_mangle_1004.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/utils/___torch_mangle_1004.py.debug_pkl
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+++ /dev/null
@@ -1,15 +0,0 @@
-class BasicConv2d(Module):
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-  training : bool
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-  conv : __torch__.torch.nn.modules.conv.___torch_mangle_1005.Conv2d
-  norm : __torch__.torch.nn.modules.normalization.___torch_mangle_1006.GroupNorm
-  act : __torch__.torch.nn.modules.activation.___torch_mangle_1007.LeakyReLU
-  def forward(self: __torch__.utils.___torch_mangle_1008.BasicConv2d,
-    input: Tensor) -> Tensor:
-    act = self.act
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-    _0 = (norm).forward((conv).forward(input, ), )
-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/utils/___torch_mangle_1008.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/utils/___torch_mangle_1008.py.debug_pkl
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+++ /dev/null
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-class ConvSC(Module):
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-    input: Tensor) -> Tensor:
-    conv = self.conv
-    return (conv).forward(input, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/utils/___torch_mangle_1009.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/utils/___torch_mangle_1009.py.debug_pkl
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+++ /dev/null
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-class BasicConv2d(Module):
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-  act : __torch__.torch.nn.modules.activation.___torch_mangle_537.LeakyReLU
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-    return (act).forward(_0, )
diff --git a/xe_weights14/glonet_p3/code/__torch__/utils/___torch_mangle_538.py.debug_pkl b/xe_weights14/glonet_p3/code/__torch__/utils/___torch_mangle_538.py.debug_pkl
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+++ /dev/null
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