Build uploaded using `kernels`.

.gitattributes

@@ -33,3 +33,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+build/torch210-cxx11-cu126-x86_64-linux/_cv_utils_8541a33.abi3.so filter=lfs diff=lfs merge=lfs -text
+build/torch210-cxx11-cu128-x86_64-linux/_cv_utils_8541a33.abi3.so filter=lfs diff=lfs merge=lfs -text
+build/torch210-cxx11-cu130-x86_64-linux/_cv_utils_8541a33.abi3.so filter=lfs diff=lfs merge=lfs -text
+build/torch28-cxx11-cu126-x86_64-linux/_cv_utils_8541a33.abi3.so filter=lfs diff=lfs merge=lfs -text
+build/torch28-cxx11-cu128-x86_64-linux/_cv_utils_8541a33.abi3.so filter=lfs diff=lfs merge=lfs -text
+build/torch28-cxx11-cu129-x86_64-linux/_cv_utils_8541a33.abi3.so filter=lfs diff=lfs merge=lfs -text
+build/torch29-cxx11-cu126-x86_64-linux/_cv_utils_8541a33.abi3.so filter=lfs diff=lfs merge=lfs -text
+build/torch29-cxx11-cu128-x86_64-linux/_cv_utils_8541a33.abi3.so filter=lfs diff=lfs merge=lfs -text
+build/torch29-cxx11-cu130-x86_64-linux/_cv_utils_8541a33.abi3.so filter=lfs diff=lfs merge=lfs -text

build/torch210-cxx11-cu126-x86_64-linux/__init__.py

@@ -0,0 +1,12 @@
+import torch
+from typing import List
+
+from ._ops import ops
+
+def cc_2d(inputs: torch.Tensor, get_counts: bool) -> List[torch.Tensor]:
+    return ops.cc_2d(inputs, get_counts)
+
+def generic_nms(dets: torch.Tensor, scores: torch.Tensor, iou_threshold: float, use_iou_matrix: bool) -> torch.Tensor:
+    return ops.generic_nms(dets, scores, iou_threshold, use_iou_matrix)
+
+__all__ = ["cc_2d", "generic_nms"]

build/torch210-cxx11-cu126-x86_64-linux/_cv_utils_8541a33.abi3.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f14cb757e0681150cf83034c996a1be36ae664aa3eed43000800a6693af9b72
+size 3049632

build/torch210-cxx11-cu126-x86_64-linux/_ops.py

@@ -0,0 +1,9 @@
+import torch
+from . import _cv_utils_8541a33
+ops = torch.ops._cv_utils_8541a33
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_cv_utils_8541a33::{op_name}"

build/torch210-cxx11-cu126-x86_64-linux/cv_utils/__init__.py

@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch210-cxx11-cu126-x86_64-linux/metadata.json

@@ -0,0 +1,4 @@
+{
+  "version": 1,
+  "python-depends": []
+}

build/torch210-cxx11-cu128-x86_64-linux/__init__.py

@@ -0,0 +1,12 @@
+import torch
+from typing import List
+
+from ._ops import ops
+
+def cc_2d(inputs: torch.Tensor, get_counts: bool) -> List[torch.Tensor]:
+    return ops.cc_2d(inputs, get_counts)
+
+def generic_nms(dets: torch.Tensor, scores: torch.Tensor, iou_threshold: float, use_iou_matrix: bool) -> torch.Tensor:
+    return ops.generic_nms(dets, scores, iou_threshold, use_iou_matrix)
+
+__all__ = ["cc_2d", "generic_nms"]

build/torch210-cxx11-cu128-x86_64-linux/_cv_utils_8541a33.abi3.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:32f9fb286f43ff4ef053a298a2b56b9372aecd0c7bd5be543da4ad8bd54220c0
+size 3735536

build/torch210-cxx11-cu128-x86_64-linux/_ops.py

@@ -0,0 +1,9 @@
+import torch
+from . import _cv_utils_8541a33
+ops = torch.ops._cv_utils_8541a33
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_cv_utils_8541a33::{op_name}"

build/torch210-cxx11-cu128-x86_64-linux/cv_utils/__init__.py

@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch210-cxx11-cu128-x86_64-linux/metadata.json

@@ -0,0 +1,4 @@
+{
+  "version": 1,
+  "python-depends": []
+}

build/torch210-cxx11-cu130-x86_64-linux/__init__.py

@@ -0,0 +1,12 @@
+import torch
+from typing import List
+
+from ._ops import ops
+
+def cc_2d(inputs: torch.Tensor, get_counts: bool) -> List[torch.Tensor]:
+    return ops.cc_2d(inputs, get_counts)
+
+def generic_nms(dets: torch.Tensor, scores: torch.Tensor, iou_threshold: float, use_iou_matrix: bool) -> torch.Tensor:
+    return ops.generic_nms(dets, scores, iou_threshold, use_iou_matrix)
+
+__all__ = ["cc_2d", "generic_nms"]

build/torch210-cxx11-cu130-x86_64-linux/_cv_utils_8541a33.abi3.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:33965955e08aa40c4fde17c0dd32ac1db509d303e26788d43476a1f33ec9f315
+size 3518272

build/torch210-cxx11-cu130-x86_64-linux/_ops.py

@@ -0,0 +1,9 @@
+import torch
+from . import _cv_utils_8541a33
+ops = torch.ops._cv_utils_8541a33
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_cv_utils_8541a33::{op_name}"

build/torch210-cxx11-cu130-x86_64-linux/cv_utils/__init__.py

@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch210-cxx11-cu130-x86_64-linux/metadata.json

@@ -0,0 +1,4 @@
+{
+  "version": 1,
+  "python-depends": []
+}

build/torch28-cxx11-cu126-x86_64-linux/__init__.py

@@ -0,0 +1,12 @@
+import torch
+from typing import List
+
+from ._ops import ops
+
+def cc_2d(inputs: torch.Tensor, get_counts: bool) -> List[torch.Tensor]:
+    return ops.cc_2d(inputs, get_counts)
+
+def generic_nms(dets: torch.Tensor, scores: torch.Tensor, iou_threshold: float, use_iou_matrix: bool) -> torch.Tensor:
+    return ops.generic_nms(dets, scores, iou_threshold, use_iou_matrix)
+
+__all__ = ["cc_2d", "generic_nms"]

build/torch28-cxx11-cu126-x86_64-linux/_cv_utils_8541a33.abi3.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:965f12c73154f2a0713869b6efc763a46c8427705429893febeb5315eca44333
+size 3038768

build/torch28-cxx11-cu126-x86_64-linux/_ops.py

@@ -0,0 +1,9 @@
+import torch
+from . import _cv_utils_8541a33
+ops = torch.ops._cv_utils_8541a33
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_cv_utils_8541a33::{op_name}"

build/torch28-cxx11-cu126-x86_64-linux/cv_utils/__init__.py

@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch28-cxx11-cu126-x86_64-linux/metadata.json

@@ -0,0 +1,4 @@
+{
+  "version": 1,
+  "python-depends": []
+}

build/torch28-cxx11-cu128-x86_64-linux/__init__.py

@@ -0,0 +1,12 @@
+import torch
+from typing import List
+
+from ._ops import ops
+
+def cc_2d(inputs: torch.Tensor, get_counts: bool) -> List[torch.Tensor]:
+    return ops.cc_2d(inputs, get_counts)
+
+def generic_nms(dets: torch.Tensor, scores: torch.Tensor, iou_threshold: float, use_iou_matrix: bool) -> torch.Tensor:
+    return ops.generic_nms(dets, scores, iou_threshold, use_iou_matrix)
+
+__all__ = ["cc_2d", "generic_nms"]

build/torch28-cxx11-cu128-x86_64-linux/_cv_utils_8541a33.abi3.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8cc8f3bb21c53b3e39cc1a05bb40b8944429523659dd12dfbb613f1fa8e1d578
+size 3720296

build/torch28-cxx11-cu128-x86_64-linux/_ops.py

@@ -0,0 +1,9 @@
+import torch
+from . import _cv_utils_8541a33
+ops = torch.ops._cv_utils_8541a33
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_cv_utils_8541a33::{op_name}"

build/torch28-cxx11-cu128-x86_64-linux/cv_utils/__init__.py

@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch28-cxx11-cu128-x86_64-linux/metadata.json

@@ -0,0 +1,4 @@
+{
+  "version": 1,
+  "python-depends": []
+}

build/torch28-cxx11-cu129-x86_64-linux/__init__.py

@@ -0,0 +1,12 @@
+import torch
+from typing import List
+
+from ._ops import ops
+
+def cc_2d(inputs: torch.Tensor, get_counts: bool) -> List[torch.Tensor]:
+    return ops.cc_2d(inputs, get_counts)
+
+def generic_nms(dets: torch.Tensor, scores: torch.Tensor, iou_threshold: float, use_iou_matrix: bool) -> torch.Tensor:
+    return ops.generic_nms(dets, scores, iou_threshold, use_iou_matrix)
+
+__all__ = ["cc_2d", "generic_nms"]

build/torch28-cxx11-cu129-x86_64-linux/_cv_utils_8541a33.abi3.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3468fd8bd586ae6aee2e0f5d3c124f99fdd14c82823f4de07208505a4646c72e
+size 3688520

build/torch28-cxx11-cu129-x86_64-linux/_ops.py

@@ -0,0 +1,9 @@
+import torch
+from . import _cv_utils_8541a33
+ops = torch.ops._cv_utils_8541a33
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_cv_utils_8541a33::{op_name}"

build/torch28-cxx11-cu129-x86_64-linux/cv_utils/__init__.py

@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch28-cxx11-cu129-x86_64-linux/metadata.json

@@ -0,0 +1,4 @@
+{
+  "version": 1,
+  "python-depends": []
+}

build/torch29-cxx11-cu126-x86_64-linux/__init__.py

@@ -0,0 +1,12 @@
+import torch
+from typing import List
+
+from ._ops import ops
+
+def cc_2d(inputs: torch.Tensor, get_counts: bool) -> List[torch.Tensor]:
+    return ops.cc_2d(inputs, get_counts)
+
+def generic_nms(dets: torch.Tensor, scores: torch.Tensor, iou_threshold: float, use_iou_matrix: bool) -> torch.Tensor:
+    return ops.generic_nms(dets, scores, iou_threshold, use_iou_matrix)
+
+__all__ = ["cc_2d", "generic_nms"]

build/torch29-cxx11-cu126-x86_64-linux/_cv_utils_8541a33.abi3.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46b1b898561385da3db612743851de4835f78c8a6338695b4bf60e908ea53712
+size 3038880

build/torch29-cxx11-cu126-x86_64-linux/_ops.py

@@ -0,0 +1,9 @@
+import torch
+from . import _cv_utils_8541a33
+ops = torch.ops._cv_utils_8541a33
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_cv_utils_8541a33::{op_name}"

build/torch29-cxx11-cu126-x86_64-linux/cv_utils/__init__.py

@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch29-cxx11-cu126-x86_64-linux/metadata.json

@@ -0,0 +1,4 @@
+{
+  "version": 1,
+  "python-depends": []
+}

build/torch29-cxx11-cu128-x86_64-linux/__init__.py

@@ -0,0 +1,12 @@
+import torch
+from typing import List
+
+from ._ops import ops
+
+def cc_2d(inputs: torch.Tensor, get_counts: bool) -> List[torch.Tensor]:
+    return ops.cc_2d(inputs, get_counts)
+
+def generic_nms(dets: torch.Tensor, scores: torch.Tensor, iou_threshold: float, use_iou_matrix: bool) -> torch.Tensor:
+    return ops.generic_nms(dets, scores, iou_threshold, use_iou_matrix)
+
+__all__ = ["cc_2d", "generic_nms"]

build/torch29-cxx11-cu128-x86_64-linux/_cv_utils_8541a33.abi3.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d5e27aabc8a0209a8f591a0159427ca375f2a636589050c5dacee2eb9e05fb9b
+size 3724424

build/torch29-cxx11-cu128-x86_64-linux/_ops.py

@@ -0,0 +1,9 @@
+import torch
+from . import _cv_utils_8541a33
+ops = torch.ops._cv_utils_8541a33
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_cv_utils_8541a33::{op_name}"

build/torch29-cxx11-cu128-x86_64-linux/cv_utils/__init__.py

@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch29-cxx11-cu128-x86_64-linux/metadata.json

@@ -0,0 +1,4 @@
+{
+  "version": 1,
+  "python-depends": []
+}

build/torch29-cxx11-cu130-x86_64-linux/__init__.py

@@ -0,0 +1,12 @@
+import torch
+from typing import List
+
+from ._ops import ops
+
+def cc_2d(inputs: torch.Tensor, get_counts: bool) -> List[torch.Tensor]:
+    return ops.cc_2d(inputs, get_counts)
+
+def generic_nms(dets: torch.Tensor, scores: torch.Tensor, iou_threshold: float, use_iou_matrix: bool) -> torch.Tensor:
+    return ops.generic_nms(dets, scores, iou_threshold, use_iou_matrix)
+
+__all__ = ["cc_2d", "generic_nms"]

build/torch29-cxx11-cu130-x86_64-linux/_cv_utils_8541a33.abi3.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0210e7555143ef4a4038d939048ce4ae20266586aeaa0b1990b8347589c921a5
+size 3506456

build/torch29-cxx11-cu130-x86_64-linux/_ops.py

@@ -0,0 +1,9 @@
+import torch
+from . import _cv_utils_8541a33
+ops = torch.ops._cv_utils_8541a33
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_cv_utils_8541a33::{op_name}"

build/torch29-cxx11-cu130-x86_64-linux/cv_utils/__init__.py

@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch29-cxx11-cu130-x86_64-linux/metadata.json

@@ -0,0 +1,4 @@
+{
+  "version": 1,
+  "python-depends": []
+}