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.venv/lib/python3.12/site-packages/torch/include/ATen/ops/vsplit_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,29 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API ::std::vector<at::Tensor> vsplit(const at::Tensor & self, int64_t sections);
+TORCH_API ::std::vector<at::Tensor> vsplit(const at::Tensor & self, at::IntArrayRef indices);
+
+} // namespace compositeimplicitautograd
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/vsplit_native.h

@@ -0,0 +1,27 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::vector<at::Tensor> vsplit(const at::Tensor & self, int64_t sections);
+TORCH_API ::std::vector<at::Tensor> vsplit(const at::Tensor & self, at::IntArrayRef indices);
+} // namespace native
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/vsplit_ops.h

@@ -0,0 +1,45 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API vsplit_int {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::vsplit";
+  static constexpr const char* overload_name = "int";
+  static constexpr const char* schema_str = "vsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, int64_t sections);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t sections);
+};
+
+struct TORCH_API vsplit_array {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, at::IntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::vsplit";
+  static constexpr const char* overload_name = "array";
+  static constexpr const char* schema_str = "vsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, at::IntArrayRef indices);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef indices);
+};
+
+}} // namespace at::_ops
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/vstack.h

@@ -0,0 +1,45 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <string_view>
+
+
+
+#include <ATen/ops/vstack_ops.h>
+
+namespace at {
+
+
+// aten::vstack(Tensor[] tensors) -> Tensor
+inline at::Tensor vstack(at::TensorList tensors) {
+    return at::_ops::vstack::call(tensors);
+}
+
+// aten::vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & vstack_out(at::Tensor & out, at::TensorList tensors) {
+    return at::_ops::vstack_out::call(tensors, out);
+}
+// aten::vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & vstack_outf(at::TensorList tensors, at::Tensor & out) {
+    return at::_ops::vstack_out::call(tensors, out);
+}
+
+}
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/vstack_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,30 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor vstack(at::TensorList tensors);
+TORCH_API at::Tensor & vstack_out(at::Tensor & out, at::TensorList tensors);
+TORCH_API at::Tensor & vstack_outf(at::TensorList tensors, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/vstack_native.h

@@ -0,0 +1,27 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor vstack(at::TensorList tensors);
+TORCH_API at::Tensor & vstack_out(at::TensorList tensors, at::Tensor & out);
+} // namespace native
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/vstack_ops.h

@@ -0,0 +1,45 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API vstack {
+  using schema = at::Tensor (at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::vstack";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "vstack(Tensor[] tensors) -> Tensor";
+  static at::Tensor call(at::TensorList tensors);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors);
+};
+
+struct TORCH_API vstack_out {
+  using schema = at::Tensor & (at::TensorList, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::vstack";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(at::TensorList tensors, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Tensor & out);
+};
+
+}} // namespace at::_ops
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/where.h

@@ -0,0 +1,65 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <string_view>
+
+
+
+#include <ATen/ops/where_ops.h>
+
+namespace at {
+
+
+// aten::where.self(Tensor condition, Tensor self, Tensor other) -> Tensor
+inline at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::where_self::call(condition, self, other);
+}
+
+// aten::where.self_out(Tensor condition, Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & where_out(at::Tensor & out, const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::where_self_out::call(condition, self, other, out);
+}
+// aten::where.self_out(Tensor condition, Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & where_outf(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::where_self_out::call(condition, self, other, out);
+}
+
+// aten::where.ScalarSelf(Tensor condition, Scalar self, Tensor other) -> Tensor
+inline at::Tensor where(const at::Tensor & condition, const at::Scalar & self, const at::Tensor & other) {
+    return at::_ops::where_ScalarSelf::call(condition, self, other);
+}
+
+// aten::where.ScalarOther(Tensor condition, Tensor self, Scalar other) -> Tensor
+inline at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Scalar & other) {
+    return at::_ops::where_ScalarOther::call(condition, self, other);
+}
+
+// aten::where.Scalar(Tensor condition, Scalar self, Scalar other) -> Tensor
+inline at::Tensor where(const at::Tensor & condition, const at::Scalar & self, const at::Scalar & other) {
+    return at::_ops::where_Scalar::call(condition, self, other);
+}
+
+// aten::where(Tensor condition) -> Tensor[]
+inline ::std::vector<at::Tensor> where(const at::Tensor & condition) {
+    return at::_ops::where::call(condition);
+}
+
+}
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/where_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,31 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor where(const at::Tensor & condition, const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor where(const at::Tensor & condition, const at::Scalar & self, const at::Scalar & other);
+TORCH_API ::std::vector<at::Tensor> where(const at::Tensor & condition);
+
+} // namespace compositeimplicitautograd
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/where_cpu_dispatch.h

@@ -0,0 +1,30 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & where_out(at::Tensor & out, const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & where_outf(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/where_cuda_dispatch.h

@@ -0,0 +1,30 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & where_out(at::Tensor & out, const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & where_outf(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/where_native.h

@@ -0,0 +1,33 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & where_self_out(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor NestedTensor_where(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & NestedTensor_where_out(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor where(const at::Tensor & condition, const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor where(const at::Tensor & condition, const at::Scalar & self, const at::Scalar & other);
+TORCH_API ::std::vector<at::Tensor> where(const at::Tensor & condition);
+} // namespace native
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/where_ops.h

@@ -0,0 +1,89 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API where_self {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::where";
+  static constexpr const char* overload_name = "self";
+  static constexpr const char* schema_str = "where.self(Tensor condition, Tensor self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API where_self_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::where";
+  static constexpr const char* overload_name = "self_out";
+  static constexpr const char* schema_str = "where.self_out(Tensor condition, Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+};
+
+struct TORCH_API where_ScalarSelf {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::where";
+  static constexpr const char* overload_name = "ScalarSelf";
+  static constexpr const char* schema_str = "where.ScalarSelf(Tensor condition, Scalar self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Tensor & condition, const at::Scalar & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & condition, const at::Scalar & self, const at::Tensor & other);
+};
+
+struct TORCH_API where_ScalarOther {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::where";
+  static constexpr const char* overload_name = "ScalarOther";
+  static constexpr const char* schema_str = "where.ScalarOther(Tensor condition, Tensor self, Scalar other) -> Tensor";
+  static at::Tensor call(const at::Tensor & condition, const at::Tensor & self, const at::Scalar & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & condition, const at::Tensor & self, const at::Scalar & other);
+};
+
+struct TORCH_API where_Scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::where";
+  static constexpr const char* overload_name = "Scalar";
+  static constexpr const char* schema_str = "where.Scalar(Tensor condition, Scalar self, Scalar other) -> Tensor";
+  static at::Tensor call(const at::Tensor & condition, const at::Scalar & self, const at::Scalar & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & condition, const at::Scalar & self, const at::Scalar & other);
+};
+
+struct TORCH_API where {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::where";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "where(Tensor condition) -> Tensor[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & condition);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & condition);
+};
+
+}} // namespace at::_ops
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/xlogy.h

@@ -0,0 +1,83 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <string_view>
+
+
+
+#include <ATen/ops/xlogy_ops.h>
+
+namespace at {
+
+
+// aten::xlogy.Tensor(Tensor self, Tensor other) -> Tensor
+inline at::Tensor xlogy(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::xlogy_Tensor::call(self, other);
+}
+
+// aten::xlogy.Scalar_Self(Scalar self, Tensor other) -> Tensor
+inline at::Tensor xlogy(const at::Scalar & self, const at::Tensor & other) {
+    return at::_ops::xlogy_Scalar_Self::call(self, other);
+}
+
+// aten::xlogy.Scalar_Other(Tensor self, Scalar other) -> Tensor
+inline at::Tensor xlogy(const at::Tensor & self, const at::Scalar & other) {
+    return at::_ops::xlogy_Scalar_Other::call(self, other);
+}
+
+// aten::xlogy_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
+inline at::Tensor & xlogy_(at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::xlogy__Tensor::call(self, other);
+}
+
+// aten::xlogy_.Scalar_Other(Tensor(a!) self, Scalar other) -> Tensor(a!)
+inline at::Tensor & xlogy_(at::Tensor & self, const at::Scalar & other) {
+    return at::_ops::xlogy__Scalar_Other::call(self, other);
+}
+
+// aten::xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::xlogy_OutTensor::call(self, other, out);
+}
+// aten::xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & xlogy_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::xlogy_OutTensor::call(self, other, out);
+}
+
+// aten::xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & xlogy_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other) {
+    return at::_ops::xlogy_OutScalar_Self::call(self, other, out);
+}
+// aten::xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & xlogy_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::xlogy_OutScalar_Self::call(self, other, out);
+}
+
+// aten::xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
+    return at::_ops::xlogy_OutScalar_Other::call(self, other, out);
+}
+// aten::xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & xlogy_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
+    return at::_ops::xlogy_OutScalar_Other::call(self, other, out);
+}
+
+}
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/xlogy_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,34 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor xlogy(const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor xlogy(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & xlogy_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+TORCH_API at::Tensor & xlogy_(at::Tensor & self, const at::Scalar & other);
+
+} // namespace compositeexplicitautograd
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/xlogy_compositeexplicitautogradnonfunctional_dispatch.h

@@ -0,0 +1,29 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor xlogy(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_(at::Tensor & self, const at::Tensor & other);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/xlogy_cpu_dispatch.h

@@ -0,0 +1,31 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor xlogy(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor & xlogy_(at::Tensor & self, const at::Tensor & other);
+
+} // namespace cpu
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/xlogy_cuda_dispatch.h

@@ -0,0 +1,31 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor xlogy(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor & xlogy_(at::Tensor & self, const at::Tensor & other);
+
+} // namespace cuda
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/xlogy_meta.h

@@ -0,0 +1,32 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_xlogy_Tensor : public TensorIteratorBase {
+
+
+    void meta(const at::Tensor & self, const at::Tensor & other);
+};
+
+} // namespace native
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/xlogy_meta_dispatch.h

@@ -0,0 +1,31 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor xlogy(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor & xlogy_(at::Tensor & self, const at::Tensor & other);
+
+} // namespace meta
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/xlogy_native.h

@@ -0,0 +1,33 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/xlogy_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_xlogy_out : public at::meta::structured_xlogy_Tensor {
+void impl(const at::Tensor & self, const at::Tensor & other, const at::Tensor & out);
+};
+TORCH_API at::Tensor xlogy(const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_out(const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor xlogy(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & xlogy_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+TORCH_API at::Tensor & xlogy_(at::Tensor & self, const at::Scalar & other);
+} // namespace native
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/xlogy_ops.h

@@ -0,0 +1,111 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API xlogy_Tensor {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::xlogy";
+  static constexpr const char* overload_name = "Tensor";
+  static constexpr const char* schema_str = "xlogy.Tensor(Tensor self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API xlogy_Scalar_Self {
+  using schema = at::Tensor (const at::Scalar &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::xlogy";
+  static constexpr const char* overload_name = "Scalar_Self";
+  static constexpr const char* schema_str = "xlogy.Scalar_Self(Scalar self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Scalar & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other);
+};
+
+struct TORCH_API xlogy_Scalar_Other {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::xlogy";
+  static constexpr const char* overload_name = "Scalar_Other";
+  static constexpr const char* schema_str = "xlogy.Scalar_Other(Tensor self, Scalar other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Scalar & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other);
+};
+
+struct TORCH_API xlogy__Tensor {
+  using schema = at::Tensor & (at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::xlogy_";
+  static constexpr const char* overload_name = "Tensor";
+  static constexpr const char* schema_str = "xlogy_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, const at::Tensor & other);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API xlogy__Scalar_Other {
+  using schema = at::Tensor & (at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::xlogy_";
+  static constexpr const char* overload_name = "Scalar_Other";
+  static constexpr const char* schema_str = "xlogy_.Scalar_Other(Tensor(a!) self, Scalar other) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, const at::Scalar & other);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other);
+};
+
+struct TORCH_API xlogy_OutTensor {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::xlogy";
+  static constexpr const char* overload_name = "OutTensor";
+  static constexpr const char* schema_str = "xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+};
+
+struct TORCH_API xlogy_OutScalar_Self {
+  using schema = at::Tensor & (const at::Scalar &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::xlogy";
+  static constexpr const char* overload_name = "OutScalar_Self";
+  static constexpr const char* schema_str = "xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+};
+
+struct TORCH_API xlogy_OutScalar_Other {
+  using schema = at::Tensor & (const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::xlogy";
+  static constexpr const char* overload_name = "OutScalar_Other";
+  static constexpr const char* schema_str = "xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+};
+
+}} // namespace at::_ops
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/xor.h

@@ -0,0 +1,41 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <string_view>
+
+
+
+#include <ATen/ops/xor_ops.h>
+
+namespace at {
+
+
+// aten::__xor__.Scalar(Tensor self, Scalar other) -> Tensor
+inline at::Tensor __xor__(const at::Tensor & self, const at::Scalar & other) {
+    return at::_ops::__xor___Scalar::call(self, other);
+}
+
+// aten::__xor__.Tensor(Tensor self, Tensor other) -> Tensor
+inline at::Tensor __xor__(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::__xor___Tensor::call(self, other);
+}
+
+}
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/xor_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,31 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor __xor__(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & __ixor__(at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor __xor__(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & __ixor__(at::Tensor & self, const at::Tensor & other);
+
+} // namespace compositeimplicitautograd
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/xor_native.h

@@ -0,0 +1,29 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor __xor__(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & __ixor__(at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor __xor__(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & __ixor__(at::Tensor & self, const at::Tensor & other);
+} // namespace native
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/xor_ops.h

@@ -0,0 +1,67 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API __xor___Scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::__xor__";
+  static constexpr const char* overload_name = "Scalar";
+  static constexpr const char* schema_str = "__xor__.Scalar(Tensor self, Scalar other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Scalar & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other);
+};
+
+struct TORCH_API __xor___Tensor {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::__xor__";
+  static constexpr const char* overload_name = "Tensor";
+  static constexpr const char* schema_str = "__xor__.Tensor(Tensor self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API __ixor___Scalar {
+  using schema = at::Tensor & (at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::__ixor__";
+  static constexpr const char* overload_name = "Scalar";
+  static constexpr const char* schema_str = "__ixor__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, const at::Scalar & other);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other);
+};
+
+struct TORCH_API __ixor___Tensor {
+  using schema = at::Tensor & (at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::__ixor__";
+  static constexpr const char* overload_name = "Tensor";
+  static constexpr const char* schema_str = "__ixor__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, const at::Tensor & other);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other);
+};
+
+}} // namespace at::_ops
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/zero.h

@@ -0,0 +1,50 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <string_view>
+
+
+
+#include <ATen/ops/zero_ops.h>
+
+namespace at {
+
+
+// aten::zero_(Tensor(a!) self) -> Tensor(a!)
+inline at::Tensor & zero_(at::Tensor & self) {
+    return at::_ops::zero_::call(self);
+}
+
+// aten::zero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & zero_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::zero_out::call(self, out);
+}
+// aten::zero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & zero_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::zero_out::call(self, out);
+}
+
+// aten::zero(Tensor self) -> Tensor
+inline at::Tensor zero(const at::Tensor & self) {
+    return at::_ops::zero::call(self);
+}
+
+}
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/zero_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,30 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor zero(const at::Tensor & self);
+TORCH_API at::Tensor & zero_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & zero_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/zero_cpu_dispatch.h

@@ -0,0 +1,28 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor & zero_(at::Tensor & self);
+
+} // namespace cpu
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/zero_cuda_dispatch.h

@@ -0,0 +1,28 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor & zero_(at::Tensor & self);
+
+} // namespace cuda
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/zero_meta_dispatch.h

@@ -0,0 +1,28 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor & zero_(at::Tensor & self);
+
+} // namespace meta
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/zero_native.h

@@ -0,0 +1,33 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor zero(const at::Tensor & self);
+TORCH_API at::Tensor & zero_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & zero_(at::Tensor & self);
+TORCH_API at::Tensor & zero_meta_(at::Tensor & self);
+TORCH_API at::Tensor & zero_nested_(at::Tensor & self);
+TORCH_API at::Tensor & zero_sparse_(at::Tensor & self);
+TORCH_API at::Tensor & zero_sparse_csr_(at::Tensor & self);
+TORCH_API at::Tensor & mkldnn_zero_(at::Tensor & self);
+} // namespace native
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/zero_ops.h

@@ -0,0 +1,56 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API zero_ {
+  using schema = at::Tensor & (at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::zero_";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "zero_(Tensor(a!) self) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self);
+};
+
+struct TORCH_API zero_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::zero";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "zero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+struct TORCH_API zero {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::zero";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "zero(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+}} // namespace at::_ops
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/zeros.h

@@ -0,0 +1,119 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <string_view>
+
+
+
+#include <ATen/ops/zeros_ops.h>
+
+namespace at {
+
+
+// aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor zeros(at::IntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::zeros::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor zeros(at::IntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::zeros::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor zeros(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::zeros::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor zeros(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::zeros::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor zeros_symint(c10::SymIntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::zeros::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor zeros(c10::SymIntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::zeros::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor zeros_symint(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::zeros::call(size, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor zeros(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::zeros::call(size, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & zeros_out(at::Tensor & out, at::IntArrayRef size) {
+    return at::_ops::zeros_out::call(c10::fromIntArrayRefSlow(size), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & zeros_out(at::Tensor & out, at::IntArrayRef size) {
+    return at::_ops::zeros_out::call(c10::fromIntArrayRefSlow(size), out);
+  }
+}
+
+// aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & zeros_outf(at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::zeros_out::call(c10::fromIntArrayRefSlow(size), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & zeros_outf(at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::zeros_out::call(c10::fromIntArrayRefSlow(size), out);
+  }
+}
+
+// aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & zeros_symint_out(at::Tensor & out, c10::SymIntArrayRef size) {
+    return at::_ops::zeros_out::call(size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & zeros_out(at::Tensor & out, c10::SymIntArrayRef size) {
+    return at::_ops::zeros_out::call(size, out);
+  }
+}
+
+// aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & zeros_symint_outf(c10::SymIntArrayRef size, at::Tensor & out) {
+    return at::_ops::zeros_out::call(size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & zeros_outf(c10::SymIntArrayRef size, at::Tensor & out) {
+    return at::_ops::zeros_out::call(size, out);
+  }
+}
+
+}
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/zeros_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,35 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor zeros(at::IntArrayRef size, at::TensorOptions options={});
+TORCH_API at::Tensor zeros(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor zeros_symint(c10::SymIntArrayRef size, at::TensorOptions options={});
+TORCH_API at::Tensor zeros_symint(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor & zeros_out(at::Tensor & out, at::IntArrayRef size);
+TORCH_API at::Tensor & zeros_outf(at::IntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor & zeros_symint_out(at::Tensor & out, c10::SymIntArrayRef size);
+TORCH_API at::Tensor & zeros_symint_outf(c10::SymIntArrayRef size, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/zeros_like.h

@@ -0,0 +1,49 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <string_view>
+
+
+
+#include <ATen/ops/zeros_like_ops.h>
+
+namespace at {
+
+
+// aten::zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
+inline at::Tensor zeros_like(const at::Tensor & self, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::zeros_like::call(self, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
+}
+// aten::zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
+inline at::Tensor zeros_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
+    return at::_ops::zeros_like::call(self, dtype, layout, device, pin_memory, memory_format);
+}
+
+// aten::zeros_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & zeros_like_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::zeros_like_out::call(self, memory_format, out);
+}
+// aten::zeros_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & zeros_like_outf(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
+    return at::_ops::zeros_like_out::call(self, memory_format, out);
+}
+
+}
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/zeros_like_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,31 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor zeros_like(const at::Tensor & self, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor zeros_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+TORCH_API at::Tensor & zeros_like_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor & zeros_like_outf(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/zeros_like_compositeimplicitautogradnestedtensor_dispatch.h

@@ -0,0 +1,29 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautogradnestedtensor {
+
+TORCH_API at::Tensor zeros_like(const at::Tensor & self, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor zeros_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+
+} // namespace compositeimplicitautogradnestedtensor
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/zeros_like_native.h

@@ -0,0 +1,27 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor zeros_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor & zeros_like_out(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+} // namespace native
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/zeros_like_ops.h

@@ -0,0 +1,45 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API zeros_like {
+  using schema = at::Tensor (const at::Tensor &, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>, ::std::optional<at::MemoryFormat>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::zeros_like";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+};
+
+struct TORCH_API zeros_like_out {
+  using schema = at::Tensor & (const at::Tensor &, ::std::optional<at::MemoryFormat>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::zeros_like";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "zeros_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+};
+
+}} // namespace at::_ops
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/zeros_native.h

@@ -0,0 +1,28 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor zeros_symint(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+TORCH_API at::Tensor & zeros_out(at::IntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor & zeros_sparse_out(at::IntArrayRef size, at::Tensor & out);
+} // namespace native
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/ops/zeros_ops.h

@@ -0,0 +1,45 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API zeros {
+  using schema = at::Tensor (c10::SymIntArrayRef, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::zeros";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor";
+  static at::Tensor call(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+};
+
+struct TORCH_API zeros_out {
+  using schema = at::Tensor & (c10::SymIntArrayRef, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::zeros";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(c10::SymIntArrayRef size, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::Tensor & out);
+};
+
+}} // namespace at::_ops
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/quantized/QTensorImpl.h

@@ -0,0 +1,130 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <ATen/quantized/Quantizer.h>
+#include <c10/core/TensorImpl.h>
+#include <c10/util/Exception.h>
+
+namespace at {
+
+/**
+ * QTensorImpl is a TensorImpl for Quantized Tensors, it stores Quantizer which
+ * specifies the quantization scheme and parameters, for more information please
+ * see ATen/quantized/Quantizer.h
+ *
+ * We'll use QTensor in code or documentation to refer to a Tensor with QTensorImpl.
+ */
+struct TORCH_API QTensorImpl : public c10::TensorImpl {
+ public:
+  QTensorImpl(
+      Storage&& storage,
+      DispatchKeySet key_set,
+      const caffe2::TypeMeta data_type,
+      QuantizerPtr quantizer);
+
+  // See Note [Enum ImplType]
+  QTensorImpl(
+      ImplType type,
+      Storage&& storage,
+      DispatchKeySet key_set,
+      const caffe2::TypeMeta data_type,
+      QuantizerPtr quantizer);
+
+
+  // TODO: Expose in PyTorch Frontend
+  QuantizerPtr quantizer() {
+    return quantizer_;
+  }
+
+  void set_quantizer_(QuantizerPtr quantizer) {
+    quantizer_ = quantizer;
+  }
+
+  /**
+   * Return a TensorImpl that is a shallow-copy of this TensorImpl.
+   *
+   * For usage of `version_counter` and `allow_tensor_metadata_change`,
+   * see NOTE [ TensorImpl Shallow-Copying ].
+   */
+  c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach(
+      const c10::VariableVersion& version_counter,
+      bool allow_tensor_metadata_change) const override {
+    auto impl = c10::make_intrusive<QTensorImpl>(
+        Storage(storage()), key_set(), data_type_, quantizer_);
+    copy_tensor_metadata(
+      /*src_q_impl=*/this,
+      /*dest_q_impl=*/impl.get(),
+      /*version_counter=*/version_counter,
+      /*allow_tensor_metadata_change=*/allow_tensor_metadata_change);
+    impl->refresh_numel();
+    impl->refresh_contiguous();
+    return impl;
+  }
+
+  /**
+   * Return a TensorImpl that is a shallow-copy of this TensorImpl.
+   *
+   * For usage of `version_counter` and `allow_tensor_metadata_change`,
+   * see NOTE [ TensorImpl Shallow-Copying ].
+   */
+  c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach(
+      c10::VariableVersion&& version_counter,
+      bool allow_tensor_metadata_change) const override {
+    auto impl = c10::make_intrusive<QTensorImpl>(
+        Storage(storage()), key_set(), data_type_, quantizer_);
+    copy_tensor_metadata(
+      /*src_q_impl=*/this,
+      /*dest_q_impl=*/impl.get(),
+      /*version_counter=*/std::move(version_counter),
+      /*allow_tensor_metadata_change=*/allow_tensor_metadata_change);
+    impl->refresh_numel();
+    impl->refresh_contiguous();
+    return impl;
+  }
+
+  /**
+   * Shallow-copies data from another TensorImpl into this TensorImpl.
+   *
+   * For why this function doesn't check this TensorImpl's `allow_tensor_metadata_change_`,
+   * see NOTE [ TensorImpl Shallow-Copying ].
+   */
+  void shallow_copy_from(const c10::intrusive_ptr<TensorImpl>& impl) override {
+    AT_ASSERT(has_compatible_shallow_copy_type(impl->key_set()));
+    auto q_impl = static_cast<const QTensorImpl*>(impl.get());
+    copy_tensor_metadata(
+      /*src_q_impl=*/q_impl,
+      /*dest_q_impl=*/this,
+      /*version_counter=*/version_counter(),
+      /*allow_tensor_metadata_change=*/allow_tensor_metadata_change());
+    refresh_numel();
+    refresh_contiguous();
+  }
+
+ private:
+  QuantizerPtr quantizer_;
+
+  const char* tensorimpl_type_name() const override;
+
+  /**
+   * Copy the tensor metadata fields (e.g. sizes / strides / storage pointer / storage_offset)
+   * from one TensorImpl to another TensorImpl.
+   *
+   * For usage of `version_counter` and `allow_tensor_metadata_change`, see NOTE [ TensorImpl Shallow-Copying ].
+   */
+  static void copy_tensor_metadata(
+      const QTensorImpl* src_q_impl,
+      QTensorImpl* dest_q_impl,
+      const c10::VariableVersion& version_counter,
+      bool allow_tensor_metadata_change) {
+    TensorImpl::copy_tensor_metadata(src_q_impl, dest_q_impl, version_counter, allow_tensor_metadata_change);
+
+    // OpaqueTensorImpl-specific fields.
+    dest_q_impl->quantizer_ = src_q_impl->quantizer_;
+  }
+};
+
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/quantized/Quantizer.h

@@ -0,0 +1,284 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <c10/core/QScheme.h>
+#include <c10/core/MemoryFormat.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <c10/util/intrusive_ptr.h>
+#include <c10/core/ScalarType.h>
+#include <c10/core/TensorOptions.h>
+
+#include <ATen/Tensor.h>
+#include <ATen/TensorUtils.h>
+
+#include <ATen/core/QuantizerBase.h>
+
+#include <cmath>
+#include <memory>
+#include <utility>
+
+namespace at {
+
+/**
+ * UnknownQuantizer is a placeholder quantizer for functions that implement
+ * quantization in a two step process.  First a tensor is allocated but with
+ * unknown quantizer, and then the quantization kernel decides what the final
+ * quantizer will be.
+ */
+struct TORCH_API UnknownQuantizer : public Quantizer {
+  explicit UnknownQuantizer(ScalarType scalar_type)
+    : Quantizer(scalar_type) {}
+
+  Tensor quantize(const Tensor& tensor) override;
+  Tensor dequantize(const Tensor& qtensor) override;
+  Tensor& dequantize_out(Tensor& rtensor, const Tensor& qtensor) override;
+  QScheme qscheme() const override;
+  bool equalTo(QuantizerPtr other) const override;
+};
+
+/**
+ * UniformQuantizer is the parent class for all uniform quantizers.
+ * These quantization scheme will map float value uniformly to
+ * the quantized value. For example, affine quantizer is
+ * the most commonly used scheme in this category.
+ */
+struct TORCH_API UniformQuantizer : public Quantizer {
+  explicit UniformQuantizer(ScalarType scalar_type) : Quantizer(scalar_type) {}
+};
+
+/**
+ * NonUniformQuantizer is the parent class for all non-uniform quantizers.
+ * These quantization scheme may map float value non-uniformly to the quantized
+ * value. K-means quantization is a representative example in this category.
+ */
+struct TORCH_API NonUniformQuantizer : public Quantizer {
+  explicit NonUniformQuantizer(ScalarType scalar_type) : Quantizer(scalar_type) {}
+};
+
+// There is also StochasticQuantizer which is uniform but not affine
+
+/**
+ * AffineQuantizer uses affine transformation to do quantization.
+ *
+ * For quantize:
+ * Y = clamp(round(X / scale + zero_point), min, max)
+ * For dequantize:
+ * X = (Y - zero_point) * scale
+ */
+struct TORCH_API AffineQuantizer : public UniformQuantizer {
+  explicit AffineQuantizer(ScalarType scalar_type) : UniformQuantizer(scalar_type) {}
+};
+
+// Note that we will not have Symmetric Quantizer in backend to reduce
+// complications in quantized kernel implementation.
+
+/**
+ * PerTensorAffineQuantizer stores a scale and a zero_point, which is used for
+ * all the values in the Tensor.
+ */
+struct TORCH_API PerTensorAffineQuantizer : public AffineQuantizer {
+  explicit PerTensorAffineQuantizer(ScalarType scalar_type, double scale, int64_t zero_point)
+    : AffineQuantizer(scalar_type),
+        scale_(scale),
+        zero_point_(zero_point) {}
+
+  Tensor quantize(const Tensor& tensor) override;
+  Tensor dequantize(const Tensor& qtensor) override;
+  Tensor& dequantize_out(Tensor& rtensor, const Tensor& qtensor) override;
+
+  QScheme qscheme() const override {
+    return kPerTensorAffine;
+  }
+
+  double scale() const {
+    return scale_;
+  }
+
+  int64_t zero_point() const {
+    return zero_point_;
+  }
+
+  bool equalTo(QuantizerPtr other) const override {
+    if (!other.get() || other->qscheme() != kPerTensorAffine) {
+      return false;
+    }
+    auto* other_per_tensor_affine =
+        static_cast<PerTensorAffineQuantizer*>(other.get());
+    return scalar_type() == other_per_tensor_affine->scalar_type() &&
+        scale() == other_per_tensor_affine->scale() &&
+        zero_point() == other_per_tensor_affine->zero_point();
+  }
+
+ private:
+  const double scale_;
+  // We use int64_t for consistency with Python
+  const int64_t zero_point_;
+};
+
+/**
+ * PerChannelAffineQuantizer is the same as PerTensorAffineQuantizer
+ * except that we have an independent scale and zero_point parameter
+ * for each channel.
+ *
+ * Also note that per channel quantization is mostly applied to output channels
+ * of weights since per-input channel of weight quantization or per-channel
+ * quantization for activations can't be efficiently supported in most of
+ * processors since it requires each multiplication result within a single
+ * dot-product to have a different scale.
+ */
+struct TORCH_API PerChannelAffineQuantizer : public AffineQuantizer {
+  explicit PerChannelAffineQuantizer(
+      ScalarType scalar_type,
+      Tensor scales,
+      Tensor zero_points,
+      int64_t axis)
+      : AffineQuantizer(scalar_type),
+        scales_(std::move(scales)),
+        zero_points_(std::move(zero_points)),
+        axis_(axis) {}
+
+  QScheme qscheme() const override {
+    return kPerChannelAffine;
+  }
+
+  Tensor scales() const {
+    return scales_;
+  }
+
+  Tensor zero_points() const {
+    return zero_points_;
+  }
+
+  int64_t axis() const {
+    return axis_;
+  }
+
+  Tensor quantize(const Tensor& tensor) override;
+  Tensor dequantize(const Tensor& qtensor) override;
+  Tensor& dequantize_out(Tensor& rtensor, const Tensor& qtensor) override;
+
+  bool equalTo(QuantizerPtr other) const override {
+    if (!other.get() || other->qscheme() != kPerChannelAffine) {
+      return false;
+    }
+    auto* other_per_channel_affine =
+        static_cast<PerChannelAffineQuantizer*>(other.get());
+    return scalar_type() == other_per_channel_affine->scalar_type() &&
+        scales().equal(other_per_channel_affine->scales()) &&
+        zero_points().equal(other_per_channel_affine->zero_points()) &&
+        axis() == other_per_channel_affine->axis();
+  }
+
+ protected:
+  Tensor scales_;
+  Tensor zero_points_;
+  const int64_t axis_;
+};
+
+/**
+ * PerChannelAffineFloatQParamsQuantizer is the same as PerChannelAffineQuantizer
+ * except that it expects both scale and zero point to be floating point values.
+ *
+ * This quantizer uses the kPerChannelAffineFloatQParams qscheme which is a variant of
+ * kPerChannelAffine.
+ *
+ * The quantize equation in this case looks like -
+ * Xq = (Xf - zero_point) * inv_scale, where inv_scale = 1.0/scale
+ *
+ * Note: Usage of floating point zero point is useful in cases where 0 doesn't need to
+ * be exactly represented in the quantized space. We can get additional precision by
+ * using floating point values for zero point.
+ */
+struct TORCH_API PerChannelAffineFloatQParamsQuantizer : public PerChannelAffineQuantizer {
+  explicit PerChannelAffineFloatQParamsQuantizer(
+      ScalarType scalar_type,
+      Tensor scales,
+      Tensor zero_points,
+      int64_t axis)
+      : PerChannelAffineQuantizer(scalar_type,
+        scales,
+        zero_points,
+        axis) {}
+
+  QScheme qscheme() const override {
+    return kPerChannelAffineFloatQParams;
+  }
+
+  Tensor quantize(const Tensor& tensor) override;
+  Tensor dequantize(const Tensor& qtensor) override;
+  Tensor& dequantize_out(Tensor& rtensor, const Tensor& qtensor) override;
+
+  bool equalTo(QuantizerPtr other) const override {
+    if (!other.get() || other->qscheme() != kPerChannelAffineFloatQParams) {
+      return false;
+    }
+    auto* other_per_channel_float_qparams =
+        static_cast<PerChannelAffineFloatQParamsQuantizer*>(other.get());
+    return scalar_type() == other_per_channel_float_qparams->scalar_type() &&
+        scales().equal(other_per_channel_float_qparams->scales()) &&
+        zero_points().equal(other_per_channel_float_qparams->zero_points()) &&
+        axis() == other_per_channel_float_qparams->axis();
+  }
+};
+
+// This is an internal utility function for getting at the QTensorImpl,
+// You should only use this for writing low level
+// setters/getters for QTensorImpl fields; otherwise, you should use
+// the low level setters/getters that were implemented using this.
+// This may be called repeatedly, so make sure it's pretty cheap.
+TORCH_API QTensorImpl* get_qtensorimpl(const TensorBase& self);
+
+// double and int64_t are because of the native function API, we only have these
+// argument types right now in native functions
+TORCH_API QuantizerPtr
+make_per_tensor_affine_quantizer(
+    double scale, int64_t zero_point, ScalarType scalar_type);
+
+TORCH_API QuantizerPtr make_per_channel_affine_quantizer(
+    const Tensor& scales,
+    const Tensor& zero_points,
+    int64_t axis,
+    ScalarType scalar_type);
+
+TORCH_API QuantizerPtr make_unknown_quantizer(ScalarType scalar_type);
+
+// Create a Quantized Tensor given arguments for normal Tensor and a quantizer
+TORCH_API Tensor new_qtensor(
+    IntArrayRef sizes,
+    const TensorOptions& options,
+    QuantizerPtr quantizer);
+
+TORCH_API void set_quantizer_(const Tensor& self, ConstQuantizerPtr quantizer);
+
+TORCH_API Tensor from_blob_quantized_per_tensor_affine(
+    void* data,
+    IntArrayRef sizes,
+    IntArrayRef strides,
+    std::function<void(void*)> deleter,
+    const float scale,
+    const int64_t zeroPoint,
+    const TensorOptions& options);
+
+TORCH_API Tensor from_blob_quantized_per_tensor_affine(
+    void* data,
+    IntArrayRef sizes,
+    std::function<void(void*)> deleter,
+    const float scale,
+    const int64_t zeroPoint,
+    const TensorOptions& options);
+
+TORCH_API Tensor from_blob_quantized_per_channel_affine(
+    void* data,
+    IntArrayRef sizes,
+    std::function<void(void*)> deleter,
+    const Tensor& scales,
+    const Tensor& zero_points,
+    const int64_t axis,
+    const TensorOptions& options);
+
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/record_function.h

@@ -0,0 +1,799 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <ATen/core/ivalue.h>
+#include <ATen/core/operator_name.h>
+#include <c10/macros/Export.h>
+#include <c10/util/SmallVector.h>
+#include <optional>
+
+#include <array>
+#include <functional>
+#include <memory>
+#include <string_view>
+#include <variant>
+
+namespace c10 {
+class TORCH_API OperatorHandle;
+}
+
+namespace at {
+
+// Function name to record NCCL metadata
+extern TORCH_API const std::string kParamCommsCallName;
+
+// Kind of record function scope;
+enum class C10_API_ENUM RecordScope : uint8_t {
+  // c10/ATen ops, autograd nodes
+  FUNCTION = 0,
+  // Functions/nodes called from the autograd
+  BACKWARD_FUNCTION,
+  // TorchScript functions, methods
+  TORCHSCRIPT_FUNCTION,
+  // Kernel Function dtype Tag
+  KERNEL_FUNCTION_DTYPE,
+  // Torchbind custom class,
+  CUSTOM_CLASS,
+  // Generic Build Feature
+  BUILD_FEATURE,
+  // Kernel Function dtype Tag
+  LITE_INTERPRETER,
+  // User defined scope (e.g. with record_function())
+  USER_SCOPE,
+  // Scopes for static runtime, a specialized TorchScript interpreter
+  STATIC_RUNTIME_OP,
+  STATIC_RUNTIME_MODEL,
+  NUM_SCOPES, // must be the last in the list
+};
+
+} // namespace at
+
+namespace std {
+template <>
+struct hash<at::RecordScope> {
+  size_t operator()(const at::RecordScope& sc) const noexcept {
+    return static_cast<std::size_t>(sc);
+  }
+};
+} // namespace std
+
+namespace at {
+
+struct TORCH_API StringView {
+  StringView() : StringView(nullptr) {}
+  explicit StringView(const char* str_ptr)
+      : owned_str_ptr_(nullptr), str_ptr_(str_ptr) {}
+  explicit StringView(std::string str)
+      : owned_str_ptr_(std::make_shared<std::string>(std::move(str))),
+        str_ptr_(owned_str_ptr_->c_str()) {}
+
+  const char* str() const {
+    return str_ptr_;
+  }
+
+  friend std::ostream& operator<<(std::ostream& os, const StringView& dt) {
+    os << dt.str();
+    return os;
+  }
+
+  friend bool operator==(const StringView& lhs, const StringView& rhs) {
+    return strcmp(lhs.str(), rhs.str()) == 0;
+  }
+
+  friend bool operator!=(const StringView& lhs, const StringView& rhs) {
+    return !(lhs == rhs);
+  }
+
+ private:
+  std::shared_ptr<std::string> owned_str_ptr_;
+  const char* str_ptr_;
+};
+
+// Soft limit on the number of callbacks to use;
+constexpr std::size_t kSoftLimitCallbacks = 4;
+
+// An abstract base class for various observer contexts that can be attached to
+// the RecordFunction.
+struct ObserverContext {
+  virtual ~ObserverContext() = default;
+
+ protected:
+  ObserverContext() = default;
+};
+
+typedef c10::SmallVector<uint64_t, kSoftLimitCallbacks> CallbackHandles;
+typedef c10::SmallVector<std::unique_ptr<ObserverContext>, kSoftLimitCallbacks>
+    ObserverContextList;
+typedef uint64_t RecordFunctionHandle;
+struct RecordFunction;
+
+//
+// PyTorch callbacks/observers API:
+//
+
+/**
+ * RecordFunctionCallback represents a pair of callbacks to be used with
+ * RecordFunction, members:
+ *   start, end - the callbacks to run when entering and exiting the scope;
+ *     optionally, the start callback may return an ObserverContext which will
+ *     be passed to the end callback, use appropriate constructor accordingly.
+ *   needs_inputs - whether the callbacks need the inputs passed from the
+ * observed function/range; NOTE: passing the inputs incurs an additional
+ * overhead; sampling_probability - if not 1.0, then the callback is
+ * probabilistically sampled to run; NOTE: start and end callbacks always run as
+ * a pair and are sampled together; scopes - types of scopes to execute the
+ * callbacks on (see RecordScope); passing empty set means the callbacks will be
+ * executed for all possible scope types should_run - optional function that
+ * returns whether this callback should run; overwrites the effect of setting
+ * sampling_probability
+ */
+class TORCH_API RecordFunctionCallback {
+ public:
+  using StartCallback =
+      std::unique_ptr<ObserverContext> (*)(const RecordFunction&);
+  using EndCallback = void (*)(const RecordFunction&, ObserverContext*);
+
+  // This interface supports observers that require passing an ObserverContext
+  // between start and end callbacks.
+  explicit RecordFunctionCallback(
+      StartCallback start,
+      EndCallback end = nullptr)
+      : start_(start), end_(end) {
+    scopes_.fill(true);
+  }
+
+  RecordFunctionCallback& needsInputs(bool needs_inputs) {
+    needs_inputs_ = needs_inputs;
+    return *this;
+  }
+
+  RecordFunctionCallback& needsOutputs(bool needs_outputs) {
+    needs_outputs_ = needs_outputs;
+    return *this;
+  }
+
+  RecordFunctionCallback& needsIds(bool needs_ids) {
+    needs_ids_ = needs_ids;
+    return *this;
+  }
+
+  RecordFunctionCallback& samplingProb(double sampling_prob) {
+    TORCH_CHECK(
+        sampling_prob >= 0.0 && sampling_prob <= 1.0,
+        "Invalid sampling probability");
+    sampling_prob_ = sampling_prob;
+    return *this;
+  }
+
+  RecordFunctionCallback& scopes(
+      const std::unordered_set<RecordScope, std::hash<RecordScope>>& scopes) {
+    if (!scopes.empty()) {
+      scopes_.fill(false);
+      for (auto sc : scopes) {
+        scopes_[static_cast<size_t>(sc)] = true;
+      }
+    } else {
+      scopes_.fill(true);
+    }
+    return *this;
+  }
+
+  bool needsInputs() const {
+    return needs_inputs_;
+  }
+
+  bool needsOutputs() const {
+    return needs_outputs_;
+  }
+
+  bool needsIds() const {
+    return needs_ids_;
+  }
+
+  double samplingProb() const {
+    return sampling_prob_;
+  }
+
+  bool checkScope(RecordScope sc) const {
+    return scopes_[(size_t)sc];
+  }
+
+  StartCallback start() const {
+    return start_;
+  }
+
+  EndCallback end() const {
+    return end_;
+  }
+
+ private:
+  StartCallback start_;
+  EndCallback end_;
+  double sampling_prob_ = 1.0;
+  std::array<bool, static_cast<size_t>(RecordScope::NUM_SCOPES)> scopes_ = {};
+  bool needs_inputs_ = false;
+  bool needs_outputs_ = false;
+  bool needs_ids_ = false;
+};
+
+// Notes:
+//  - two types of callbacks are provided: thread local and global
+//     - thread local callbacks are added/removed only for the given thread
+//       and are stored locally for each thread and separately from the list
+//       of the global callbacks
+//     - global callbacks are stored in a single per process list and are
+//       invoked by every RecordFunction, in addition to the thread local
+//       callbacks specific to the given thread
+//  - we allow the added callbacks to be sampled, by specifying a sampling
+//    probability for each callback pair, if the start callback is
+//    not picked to run, the corresponding end callback won't be called
+//  - a typical use case for the global callbacks is passive monitoring
+//    in the background (e.g. fleet-wide monitoring), without focusing on
+//    the specific piece of code
+//  - in contrast, thread local callbacks are enabled locally, on demand,
+//    for the specific piece of code (range) and are not sampled
+//  - a typical use case for thread local callbacks is profiler and code
+//    execution tracer
+//  - note, thread local callbacks are automatically propagated with
+//    ThreadLocalState across JIT continuations and async tasks (at::launch)
+
+typedef uint64_t CallbackHandle;
+
+constexpr CallbackHandle INVALID_CALLBACK_HANDLE{0};
+
+// It is unnecessary to use atomic operations for enabling
+// thread-local function callbacks. Moreover, it prevents saving to
+// ThreadLocalState because std::atomic is non-copyable.
+struct RecordFunctionCallbacksEntry {
+  RecordFunctionCallbacksEntry(RecordFunctionCallback cb, CallbackHandle h)
+      : callback_(cb), handle_(h) {}
+
+  RecordFunctionCallback callback_;
+  bool enabled_{true};
+  CallbackHandle handle_;
+};
+
+// Holds pairs (callbacks, unique_id)
+using RecordFunctionCallbacks = std::vector<RecordFunctionCallbacksEntry>;
+
+// Generated by the callback managers to determine which functions to run.
+struct StepCallbacks {
+  StepCallbacks() = default;
+  StepCallbacks(uint64_t thread_id, RecordScope scope)
+      : thread_id_{thread_id}, scope_{scope} {}
+
+  bool empty() const {
+    return callbacks_.empty();
+  }
+
+  struct StartEndPair {
+    RecordFunctionCallback::StartCallback start_;
+    RecordFunctionCallback::EndCallback end_;
+  };
+
+  using StartEndPairs = c10::SmallVector<StartEndPair, kSoftLimitCallbacks>;
+
+  StartEndPairs callbacks_;
+  uint64_t thread_id_{0};
+  RecordScope scope_{RecordScope::FUNCTION};
+  bool needs_inputs_{false};
+  bool needs_outputs_{false};
+  bool needs_ids_{false};
+};
+
+struct TORCH_API RecordFunction {
+  // Default constructor is used with before function called afterwards:
+  //  scope - record scope that this function tracks
+  //  pre_sampled - whether this RecordFunction was already pre-sampled with
+  //    kLowProb probability
+  explicit RecordFunction(RecordScope scope = RecordScope::FUNCTION);
+  explicit RecordFunction(StepCallbacks&& step_callbacks);
+
+  using schema_ref_t = std::reference_wrapper<const c10::FunctionSchema>;
+  using FunctionDescriptor = std::variant<std::string_view, schema_ref_t>;
+
+  void before(
+      FunctionDescriptor fn,
+      c10::ArrayRef<const c10::IValue> args,
+      int64_t current_sequence_nr = -1) {
+    if (!isActive()) {
+      return;
+    }
+    inputs_ = args;
+    before(fn, current_sequence_nr);
+  }
+
+  void before(
+      FunctionDescriptor fn,
+      c10::ArrayRef<const c10::IValue> args,
+      const std::unordered_map<std::string, IValue>* kwargs,
+      int64_t current_sequence_nr = -1) {
+    if (!isActive()) {
+      return;
+    }
+    kwinputs_ = *kwargs;
+    before(fn, args, current_sequence_nr);
+  }
+
+  void before(
+      FunctionDescriptor fn,
+      const std::unordered_map<std::string, IValue>* kwargs,
+      int64_t current_sequence_nr = -1) {
+    if (!isActive()) {
+      return;
+    }
+    kwinputs_ = *kwargs;
+    before(fn, current_sequence_nr);
+  }
+
+  void before(
+      FunctionDescriptor fn,
+      const std::vector<IValue>* args,
+      int64_t current_sequence_nr = -1) {
+    before(
+        fn,
+        c10::ArrayRef<const c10::IValue>(args->data(), args->size()),
+        current_sequence_nr);
+  }
+
+  void before(
+      FunctionDescriptor fn,
+      const std::vector<IValue>* args,
+      const std::unordered_map<std::string, IValue>* kwargs,
+      int64_t current_sequence_nr = -1) {
+    if (!isActive()) {
+      return;
+    }
+    kwinputs_ = *kwargs;
+    before(std::move(fn), args, current_sequence_nr);
+  }
+
+  // Destructor calls end callbacks
+  virtual ~RecordFunction();
+
+  RecordFunction(const RecordFunction&) = delete;
+  RecordFunction& operator=(const RecordFunction&) = delete;
+  RecordFunction(RecordFunction&&) = delete;
+  RecordFunction& operator=(RecordFunction&&) = delete;
+
+  const char* name() const;
+  const char* overload_name() const;
+
+  int64_t seqNr() const {
+    return sequence_nr_;
+  }
+
+  c10::ArrayRef<const IValue> inputs() const {
+#ifndef NDEBUG
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        inputs_valid_, "Called inputs() outside RecordFunction start callback");
+#endif
+    return inputs_;
+  }
+
+  std::unordered_map<std::string, IValue> kwinputs() const {
+#ifndef NDEBUG
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        inputs_valid_,
+        "Called kwinputs() outside RecordFunction start callback");
+#endif
+    return kwinputs_;
+  }
+
+  const std::vector<c10::IValue>& outputs() const {
+    return outputs_;
+  }
+
+  void setOutputs(std::vector<c10::IValue>&& outputs) {
+    outputs_ = std::move(outputs);
+  }
+
+  void setOutputs(c10::ArrayRef<c10::IValue> outputs) {
+    outputs_ = outputs.vec();
+  }
+
+  size_t num_inputs() const;
+  size_t num_outputs() const;
+
+  // Retrieves the thread_id that this RecordFunction ran start callbacks with.
+  // Useful for writing thread safe end callbacks that may be potentially
+  // executed in a different thread (async ops)
+  uint64_t threadId() const {
+    return step_callbacks_.thread_id_;
+  }
+
+  // For backward functions - thread id of the corresponding forward function,
+  // or zero otherwise;
+  // used alongside with sequence number to correlate backward functions with
+  // the forward ones
+  uint64_t forwardThreadId() const {
+    return fwd_thread_id_;
+  }
+
+  void setForwardThreadId(uint64_t thread_id) {
+    fwd_thread_id_ = thread_id;
+  }
+
+  RecordScope scope() const {
+    return step_callbacks_.scope_;
+  }
+
+  // Returns logical thread_id for the current thread
+  static uint64_t currentThreadId();
+
+  // Internal functions, do not use directly;
+  // used in python's context manager
+
+  // before functions initialize RecordFunction members and call
+  // start callbacks
+  void before(FunctionDescriptor schema, int64_t sequence_nr = -1);
+
+  // Sets node ID for distributed profiling
+  static void setDefaultNodeId(int64_t defaultNodeId);
+  // Gets node ID for distributed profiling
+  static int64_t getDefaultNodeId();
+
+  // Calls end callbacks. After end(), accessors will no longer provide useful
+  // results.
+  void end();
+
+  // Internal-only, used only force async event for distributed events
+  // profiling.
+  void _setAsync();
+
+  // Returns whether this RecordFunction corresponds to an async event or not.
+  bool isAsync() const;
+
+  // Returns whether this RecordFunction corresponds to NCCL metadata collection
+  // or not.
+  bool isNcclMeta() const {
+    return is_nccl_meta_;
+  }
+
+  // Internal-only, used to denote out variant used for Static Runtime execution
+  void _setStaticRuntimeOutVariant();
+  bool isStaticRuntimeOutVariant() const;
+
+  RecordFunctionHandle handle() const {
+    return handle_;
+  }
+
+  std::optional<OperatorName> operator_name() const;
+
+  // This method returns a copy of the FunctionSchema and can be expensive.
+  std::optional<FunctionSchema> operator_schema() const;
+
+  void setHandle(RecordFunctionHandle handle) {
+    handle_ = handle;
+  }
+
+  // Whether this RecordFunction runs any callbacks.
+  bool isActive() const {
+    return !step_callbacks_.empty();
+  }
+
+  bool needsInputs() const {
+    return step_callbacks_.needs_inputs_;
+  }
+
+  bool needsOutputs() const {
+    return step_callbacks_.needs_outputs_;
+  }
+
+  int64_t debugHandle() const {
+    return debug_handle_;
+  }
+
+  void setDebugHandle(int64_t debug_handle) {
+    debug_handle_ = debug_handle;
+  }
+
+  void invalidateInputs() {
+#ifndef NDEBUG
+    inputs_valid_ = false;
+#endif
+  }
+
+ private:
+  void runStartCallbacks();
+
+  StepCallbacks step_callbacks_;
+
+  // In cases when RecordFunction might be active but we chose not to
+  // use the observers (e.g. operator is not observed), this boolean
+  // flag is used to check whether the start callbacks were called
+  bool called_start_callbacks_ = false;
+
+#ifndef NDEBUG
+  bool inputs_valid_ = false;
+#endif
+
+  // Stores various ObserverContext objects with event metadata for callbacks.
+  ObserverContextList ctx_;
+
+  std::variant<std::string, schema_ref_t> fn_;
+
+  int64_t sequence_nr_ = -1;
+  c10::ArrayRef<const IValue> inputs_;
+  std::unordered_map<std::string, IValue> kwinputs_;
+  std::vector<c10::IValue> outputs_;
+
+  // For backward functions - thread id of the forward function
+  uint64_t fwd_thread_id_ = 0;
+
+  // Unique id for this RecordFunction, used in callbacks to track start
+  // and end of ranges
+  RecordFunctionHandle handle_{0};
+
+  // Whether this record_function corresponds to an async event or not. Async
+  // events can complete in different threads or follow a future-like pattern
+  // of use.
+  bool is_async_{false};
+
+  // Debug handles are used for lazy annotation of module hierarchy
+  // and callstack.
+  // This is specifically is useful for mobile runtime, where generated
+  // debug handles can be lazily symbolicated using debug information
+  int64_t debug_handle_{-1};
+
+  // Whether this RecordFunction is used for an out variant run with
+  // Static Runtime
+  bool is_static_runtime_out_variant_{false};
+
+  // Whether this RecordFunction is used for NCCL metadata collection
+  bool is_nccl_meta_{false};
+};
+
+TORCH_API StepCallbacks getStepCallbacks(RecordScope scope);
+
+TORCH_API std::optional<StepCallbacks> getStepCallbacksUnlessEmpty(
+    RecordScope scope);
+
+namespace detail {
+template <typename Inputs, typename... Args>
+void record_function_with_scope(
+    RecordFunction& guard,
+    RecordFunction::FunctionDescriptor fn,
+    const Inputs& inputs,
+    Args&&... args) {
+  if (guard.needsInputs()) {
+    guard.before(
+        fn,
+        c10::ArrayRef<const c10::IValue>(inputs.data(), inputs.size()),
+        std::forward<Args>(args)...);
+  } else {
+    guard.before(fn, std::forward<Args>(args)...);
+  }
+}
+
+template <typename Inputs, typename... Args>
+void record_function_with_scope_and_debug_handle(
+    RecordFunction& guard,
+    RecordFunction::FunctionDescriptor fn,
+    int64_t debug_handle,
+    const Inputs& inputs,
+    Args&&... args) {
+  guard.setDebugHandle(debug_handle);
+  if (guard.needsInputs()) {
+    guard.before(
+        fn,
+        c10::ArrayRef<const c10::IValue>(inputs.data(), inputs.size()),
+        std::forward<Args>(args)...);
+  } else {
+    guard.before(fn, std::forward<Args>(args)...);
+  }
+}
+
+template <typename... Args>
+void record_function_with_scope(
+    RecordFunction& guard,
+    RecordFunction::FunctionDescriptor fn,
+    c10::ArrayRef<const c10::IValue> inputs,
+    Args&&... args) {
+  return record_function_with_scope<c10::ArrayRef<const c10::IValue>, Args...>(
+      guard, fn, inputs, std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+void record_function_with_scope_and_debug_handle(
+    RecordFunction& guard,
+    RecordFunction::FunctionDescriptor fn,
+    int64_t debug_handle,
+    c10::ArrayRef<const c10::IValue> inputs,
+    Args&&... args) {
+  return record_function_with_scope_and_debug_handle<
+      c10::ArrayRef<const c10::IValue>,
+      Args...>(guard, fn, debug_handle, inputs, std::forward<Args>(args)...);
+}
+
+} // namespace detail
+
+// optional argument - function's seq_no
+#define RECORD_FUNCTION_WITH_SCOPE(scope, fn, inputs, ...) \
+  at::RecordFunction guard(scope);                         \
+  if (guard.isActive()) {                                  \
+    ::at::detail::record_function_with_scope(              \
+        guard, fn, inputs, ##__VA_ARGS__);                 \
+  }
+
+#define RECORD_FUNCTION_WITH_SCOPE_INPUTS_OUTPUTS( \
+    scope, fn, inputs, outputs, ...)               \
+  at::RecordFunction guard(scope);                 \
+  if (guard.isActive()) {                          \
+    if (guard.needsInputs()) {                     \
+      guard.before(fn, inputs, ##__VA_ARGS__);     \
+    } else {                                       \
+      guard.before(fn, ##__VA_ARGS__);             \
+    }                                              \
+    if (guard.needsOutputs()) {                    \
+      guard.setOutputs(outputs);                   \
+    }                                              \
+  }
+
+#define RECORD_FUNCTION(fn, inputs, ...) \
+  RECORD_FUNCTION_WITH_SCOPE(            \
+      at::RecordScope::FUNCTION, fn, inputs, ##__VA_ARGS__)
+
+#define RECORD_TORCHSCRIPT_FUNCTION(mn, inputs) \
+  RECORD_FUNCTION_WITH_SCOPE(at::RecordScope::TORCHSCRIPT_FUNCTION, mn, inputs)
+
+#define RECORD_FUNCTION_WITH_INPUTS_OUTPUTS(fn, inputs, outputs, ...) \
+  RECORD_FUNCTION_WITH_SCOPE_INPUTS_OUTPUTS(                          \
+      at::RecordScope::FUNCTION, fn, inputs, outputs, ##__VA_ARGS__)
+
+// Custom user scopes in C++; similar to Python's 'with record_function("..."):'
+#define RECORD_USER_SCOPE(fn) \
+  RECORD_FUNCTION_WITH_SCOPE( \
+      at::RecordScope::USER_SCOPE, fn, c10::ArrayRef<const c10::IValue>{})
+
+// RECORD_USER_SCOPE with inputs
+#define RECORD_USER_SCOPE_WITH_INPUTS(fn, inputs) \
+  RECORD_FUNCTION_WITH_SCOPE(at::RecordScope::USER_SCOPE, fn, inputs)
+
+#define RECORD_USER_SCOPE_WITH_KWARGS_ONLY(fn, kwargs) \
+  RECORD_FUNCTION_WITH_SCOPE(                          \
+      at::RecordScope::USER_SCOPE,                     \
+      fn,                                              \
+      c10::ArrayRef<const c10::IValue>{},              \
+      kwargs)
+
+// Helper macro to pass in debug handle that is used to
+// post process events
+#define RECORD_WITH_SCOPE_DEBUG_HANDLE_AND_INPUTS(             \
+    scope, fn, debug_handle, inputs, ...)                      \
+  at::RecordFunction guard(scope);                             \
+  if (guard.isActive()) {                                      \
+    ::at::detail::record_function_with_scope_and_debug_handle( \
+        guard, fn, debug_handle, inputs, ##__VA_ARGS__);       \
+  }
+
+// Helper macros to record LITE INTERPRETER scope events with debug handles
+#define RECORD_EDGE_SCOPE_WITH_DEBUG_HANDLE_AND_INPUTS( \
+    fn, debug_handle, inputs)                           \
+  RECORD_WITH_SCOPE_DEBUG_HANDLE_AND_INPUTS(            \
+      at::RecordScope::LITE_INTERPRETER, fn, debug_handle, inputs)
+
+// Bookend to the RECORD_FUNCTION macros.  Use this after the kernel
+// launch to let the profiler bind the outputs to the op that produced
+// them.  Note that guard is declared by RECORD_FUNCTION so this macro
+// needs to be called from the same scope as RECORD_FUNCTION
+#define RECORD_OUTPUTS(outputs)                                    \
+  if (guard.needsOutputs()) {                                      \
+    guard.setOutputs(                                              \
+        std::vector<c10::IValue>(outputs.begin(), outputs.end())); \
+  }
+
+/**
+ * addThreadLocalCallback adds a thread local callback to run with
+ * RecordFunction, returns handle to use with removeThreadLocalCallback
+ */
+TORCH_API CallbackHandle addThreadLocalCallback(RecordFunctionCallback cb);
+
+/**
+ * hasThreadLocalCallbacks returns whether there're callbacks registered
+ * with addThreadLocalCallback
+ */
+TORCH_API bool hasThreadLocalCallbacks();
+
+/**
+ * clearThreadLocalCallbacks removes all thread local callbacks
+ */
+TORCH_API void clearThreadLocalCallbacks();
+
+/**
+ * addGlobalCallback adds a global callback to run with RecordFunction:
+ *
+ * only during the program initialization
+ */
+TORCH_API CallbackHandle addGlobalCallback(RecordFunctionCallback cb);
+
+/**
+ * removeCallback removes a callback given the handle returned by
+ * addThreadLocalCallback or addGlobalCallback;
+ *
+ * no other code can run simultaneously
+ */
+TORCH_API void removeCallback(CallbackHandle handle);
+
+/**
+ * Prevent the given callback from executing. If handle is invalid,
+ * does nothing.
+ */
+TORCH_API void disableCallback(CallbackHandle handle);
+
+/**
+ * Allow the given callback, previously disabled with disableCallback, to
+ * execute again. If handle is invalid, does nothing.
+ */
+TORCH_API void reenableCallback(CallbackHandle handle);
+
+/**
+ * hasGlobalCallbacks returns whether there're global callbacks
+ * registered with pushGlobalCallback
+ */
+TORCH_API bool hasGlobalCallbacks();
+
+/**
+ * clearGlobalCallbacks removes all global callbacks
+ */
+TORCH_API void clearGlobalCallbacks();
+
+// for both thread local and global callbacks
+TORCH_API bool hasCallbacks();
+TORCH_API void clearCallbacks();
+
+/**
+ * enableRecordFunction enables RecordFunction thread locally
+ */
+TORCH_API void enableRecordFunction(bool enable = true);
+
+/**
+ * isRecordFunctionEnabled returns whether RecordFunction
+ * is enabled thread locally
+ */
+TORCH_API bool isRecordFunctionEnabled();
+
+class TORCH_API RecordFunctionGuard {
+ public:
+  explicit RecordFunctionGuard(bool is_enabled = true)
+      : prev_value_(isRecordFunctionEnabled()) {
+    enableRecordFunction(is_enabled);
+  }
+
+  RecordFunctionGuard(RecordFunctionGuard&& other) = delete;
+  RecordFunctionGuard(const RecordFunctionGuard&) = delete;
+  RecordFunctionGuard& operator=(const RecordFunctionGuard&) = delete;
+  RecordFunctionGuard& operator=(RecordFunctionGuard&&) = delete;
+  virtual ~RecordFunctionGuard() {
+    enableRecordFunction(prev_value_);
+  }
+
+ private:
+  bool prev_value_ = false;
+};
+
+class TORCH_API DisableRecordFunctionGuard : public RecordFunctionGuard {
+ public:
+  DisableRecordFunctionGuard() : RecordFunctionGuard(false) {}
+  ~DisableRecordFunctionGuard() override = default;
+};
+
+struct TORCH_API RecordFunctionTLS {
+  // Thread local vector of callbacks, holds pairs (callbacks, unique_id);
+  // must be sorted in increasing handles order
+  RecordFunctionCallbacks sorted_tls_callbacks_;
+
+  bool tls_record_function_enabled_ = true;
+};
+
+TORCH_API const RecordFunctionTLS& get_record_function_tls_();
+
+TORCH_API void set_record_function_tls_(const RecordFunctionTLS& tls);
+
+TORCH_API void set_record_function_seed_for_testing(uint32_t seed);
+
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/xpu/CachingHostAllocator.h

@@ -0,0 +1,43 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <ATen/core/CachingHostAllocator.h>
+#include <ATen/xpu/XPUEvent.h>
+#include <c10/core/Allocator.h>
+#include <c10/util/Deprecated.h>
+#include <c10/xpu/XPUStream.h>
+
+namespace at::xpu {
+
+C10_DEPRECATED_MESSAGE(
+    "at::xpu::getCachingHostAllocator() is deprecated. Please use at::getHostAllocator(at::kXPU) instead.")
+inline TORCH_XPU_API at::HostAllocator* getCachingHostAllocator() {
+  return at::getHostAllocator(at::kXPU);
+}
+
+C10_DEPRECATED_MESSAGE(
+    "at::xpu::CachingHostAllocator_recordEvent(...) is deprecated. Please use at::getHostAllocator(at::kXPU)->record_event(...) instead.")
+inline TORCH_XPU_API bool CachingHostAllocator_recordEvent(
+    void* ptr,
+    void* ctx,
+    c10::xpu::XPUStream stream) {
+  return getHostAllocator(at::kXPU)->record_event(ptr, ctx, stream.unwrap());
+}
+
+C10_DEPRECATED_MESSAGE(
+    "at::xpu::CachingHostAllocator_emptyCache() is deprecated. Please use at::getHostAllocator(at::kXPU)->empty_cache() instead.")
+inline TORCH_XPU_API void CachingHostAllocator_emptyCache() {
+  getHostAllocator(at::kXPU)->empty_cache();
+}
+
+C10_DEPRECATED_MESSAGE(
+    "at::xpu::HostAlloc(...) is deprecated. Please use at::getHostAllocator(at::kXPU)->allocate(...) instead.")
+inline TORCH_XPU_API at::DataPtr HostAlloc(size_t size) {
+  return getHostAllocator(at::kXPU)->allocate(size);
+}
+
+} // namespace at::xpu
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/xpu/EmptyTensor.h

@@ -0,0 +1,47 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+#include <ATen/core/TensorBase.h>
+
+namespace at::detail {
+
+TORCH_XPU_API TensorBase empty_xpu(
+    IntArrayRef size,
+    ScalarType dtype,
+    std::optional<Device> device_opt,
+    std::optional<c10::MemoryFormat> memory_format_opt);
+
+TORCH_XPU_API TensorBase empty_xpu(
+    IntArrayRef size,
+    std::optional<ScalarType> dtype_opt,
+    std::optional<Layout> layout_opt,
+    std::optional<Device> device_opt,
+    std::optional<bool> pin_memory_opt,
+    std::optional<c10::MemoryFormat> memory_format_opt);
+
+TORCH_XPU_API TensorBase
+empty_xpu(IntArrayRef size, const TensorOptions& options);
+
+TORCH_XPU_API TensorBase empty_strided_xpu(
+    IntArrayRef size,
+    IntArrayRef stride,
+    ScalarType dtype,
+    std::optional<Device> device_opt);
+
+TORCH_XPU_API TensorBase empty_strided_xpu(
+    IntArrayRef size,
+    IntArrayRef stride,
+    std::optional<ScalarType> dtype_opt,
+    std::optional<Layout> layout_opt,
+    std::optional<Device> device_opt,
+    std::optional<bool> pin_memory_opt);
+
+TORCH_XPU_API TensorBase empty_strided_xpu(
+    IntArrayRef size,
+    IntArrayRef stride,
+    const TensorOptions& options);
+
+} // namespace at::detail
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/xpu/MemPool.h

@@ -0,0 +1,43 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <c10/core/Allocator.h>
+#include <c10/xpu/XPUCachingAllocator.h>
+
+namespace at::xpu {
+
+// Keep BC only
+using c10::CaptureId_t;
+using c10::MempoolId_t;
+
+struct TORCH_XPU_API MemPool {
+  MemPool(
+      XPUCachingAllocator::XPUAllocator* allocator = nullptr,
+      bool is_user_created = true,
+      bool use_on_oom = false);
+
+  C10_DISABLE_COPY_AND_ASSIGN(MemPool);
+  MemPool(MemPool&&) = default;
+  MemPool& operator=(MemPool&&) = default;
+  ~MemPool();
+
+  MempoolId_t id();
+  XPUCachingAllocator::XPUAllocator* allocator();
+  int use_count();
+  c10::DeviceIndex device();
+  static MempoolId_t graph_pool_handle(bool is_user_created = true);
+
+ private:
+  static std::atomic<CaptureId_t> uid_;
+  static std::atomic<CaptureId_t> uuid_;
+  XPUCachingAllocator::XPUAllocator* allocator_;
+  bool is_user_created_;
+  MempoolId_t id_;
+  c10::DeviceIndex device_;
+};
+
+} // namespace at::xpu
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/xpu/PeerToPeerAccess.h

@@ -0,0 +1,29 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <c10/xpu/PeerToPeerAccess.h>
+
+#include <ATen/Context.h>
+
+namespace at::xpu {
+namespace detail {
+// Initialize the peer-to-peer access cache for XPU devices.
+inline void init_p2p_access_cache(c10::DeviceIndex num_devices) {
+  c10::xpu::detail::init_p2p_access_cache(num_devices);
+}
+} // namespace detail
+
+// Query if peer-to-peer access is available between two devices.
+// This wrapper ensures XPU lazy initialization before forwarding to c10.
+inline bool get_p2p_access(
+    c10::DeviceIndex dev,
+    c10::DeviceIndex dev_to_access) {
+  at::globalContext().lazyInitDevice(c10::DeviceType::XPU);
+  return c10::xpu::get_p2p_access(dev, dev_to_access);
+}
+
+} // namespace at::xpu
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

.venv/lib/python3.12/site-packages/torch/include/ATen/xpu/PhiloxXpuState.h

@@ -0,0 +1,50 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+namespace at {
+
+struct PhiloxXpuState {
+  PhiloxXpuState() = default;
+  PhiloxXpuState(uint64_t seed, uint64_t offset) {
+    seed_.val = seed;
+    offset_.val = offset;
+  }
+  // for graph capture
+  PhiloxXpuState(
+      int64_t* seed,
+      int64_t* offset_extragraph,
+      uint32_t offset_intragraph) {
+    seed_.ptr = seed;
+    offset_.ptr = offset_extragraph;
+    offset_intragraph_ = offset_intragraph;
+    captured_ = true;
+  }
+
+  union Payload {
+    uint64_t val;
+    int64_t* ptr;
+  };
+
+  Payload seed_{};
+  Payload offset_{};
+  uint32_t offset_intragraph_ = 0;
+  bool captured_ = false;
+};
+
+namespace xpu::philox {
+inline std::tuple<uint64_t, uint64_t> unpack(at::PhiloxXpuState arg) {
+  if (arg.captured_) {
+    return std::make_tuple(
+        static_cast<uint64_t>(*arg.seed_.ptr),
+        static_cast<uint64_t>(*(arg.offset_.ptr) + arg.offset_intragraph_));
+  } else {
+    return std::make_tuple(arg.seed_.val, arg.offset_.val);
+  }
+}
+
+} // namespace xpu::philox
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)