overrides.py

"""
Python implementation of ``__torch_function__``

While most of the torch API and handling for ``__torch_function__`` happens
at the C++ level, some of the torch API is written in Python so we need
python-level handling for ``__torch_function__`` overrides as well. The main
developer-facing functionality in this file are handle_torch_function and
has_torch_function. See torch/functional.py and test/test_overrides.py
for usage examples.

Note
----
heavily inspired by NumPy's ``__array_function__`` (see:
https://github.com/pytorch/pytorch/issues/24015 and
https://www.numpy.org/neps/nep-0018-array-function-protocol.html
)

If changing this file in a way that can affect ``__torch_function__`` overhead,
please report the benchmarks in ``benchmarks/overrides_benchmark``. See the
instructions in the ``README.md`` in that directory.
"""

import __future__

import collections
import functools
import types
import warnings
from typing import Dict, Set, List, Any, Callable, Iterable, Type, Iterator, Tuple
import contextlib

import torch
from torch._C import (
    _has_torch_function, _has_torch_function_unary,
    _has_torch_function_variadic, _add_docstr, _set_torch_function_mode, _get_torch_function_mode)

from torch.utils._mode_utils import _enable_mode, _push_mode, _ModeInfo, _wrap_init, _restore_mode

__all__ = [
    "get_ignored_functions",
    "get_overridable_functions",
    "get_testing_overrides",
    "handle_torch_function",
    "has_torch_function",
    "resolve_name",
    "is_tensor_like",
    "is_tensor_method_or_property",
    "wrap_torch_function",
    "enable_reentrant_dispatch",
    "get_buffer",
]

@functools.lru_cache(None)
def get_ignored_functions() -> Set[Callable]:
    """
    Return public functions that cannot be overridden by ``__torch_function__``.

    Returns
    -------
    Set[Callable]
        A tuple of functions that are publicly available in the torch API but cannot
        be overridden with ``__torch_function__``. Mostly this is because none of the
        arguments of these functions are tensors or tensor-likes.

    Examples
    --------
    >>> torch.Tensor.as_subclass in torch.overrides.get_ignored_functions()
    True
    >>> torch.add in torch.overrides.get_ignored_functions()
    False
    """
    Tensor = torch.Tensor
    return {
        torch.typename,
        torch.is_tensor,
        torch.is_storage,
        torch.set_default_tensor_type,
        torch.set_rng_state,
        torch.get_rng_state,
        torch.manual_seed,
        torch.initial_seed,
        torch.seed,
        torch.save,
        torch.load,
        torch.set_printoptions,
        torch.fork,
        torch.get_default_dtype,
        torch.get_num_interop_threads,
        torch.get_num_threads,
        torch.init_num_threads,
        torch.import_ir_module,
        torch.import_ir_module_from_buffer,
        torch.is_anomaly_enabled,
        torch.is_grad_enabled,
        torch.merge_type_from_type_comment,
        torch.parse_ir,
        torch.parse_schema,
        torch.parse_type_comment,
        torch.set_anomaly_enabled,
        torch.set_flush_denormal,
        torch.set_num_interop_threads,
        torch.set_num_threads,
        torch.wait,
        torch.as_tensor,
        torch.from_numpy,
        torch.get_device,
        torch.tensor,
        torch.default_generator,
        torch.has_cuda,
        torch.has_cudnn,
        torch.has_lapack,
        torch.device,
        torch.dtype,
        torch.finfo,
        torch.has_mkl,
        torch.has_mps,
        torch.has_mkldnn,
        torch.has_openmp,
        torch.iinfo,
        torch.memory_format,
        torch.qscheme,
        torch.set_grad_enabled,
        torch.no_grad,
        torch.enable_grad,
        torch.inference_mode,
        torch.is_inference_mode_enabled,
        torch.layout,
        torch.align_tensors,
        torch.arange,
        torch.as_strided,
        torch.bartlett_window,
        torch.blackman_window,
        torch.broadcast_shapes,
        torch.can_cast,
        torch.cudnn_affine_grid_generator,
        torch.cudnn_batch_norm,
        torch.cudnn_convolution,
        torch.cudnn_convolution_transpose,
        torch.cudnn_convolution_relu,
        torch.cudnn_convolution_add_relu,
        torch.cudnn_grid_sampler,
        torch.cudnn_is_acceptable,
        torch.empty,
        torch.empty_strided,
        torch.empty_quantized,
        torch.eye,
        torch.fft.fftfreq,
        torch.fft.rfftfreq,
        torch.from_file,
        torch.full,
        torch.fill,
        torch.hamming_window,
        torch.hann_window,
        torch.kaiser_window,
        torch.linspace,
        torch.logspace,
        torch.mkldnn_adaptive_avg_pool2d,
        torch.mkldnn_convolution,
        torch.mkldnn_max_pool2d,
        torch.mkldnn_max_pool3d,
        torch.mkldnn_linear_backward_weights,
        torch.nested_tensor,
        torch.normal,
        torch.ones,
        torch.promote_types,
        torch.rand,
        torch.randn,
        torch.randint,
        torch.randperm,
        torch.range,
        torch.result_type,
        torch.scalar_tensor,
        torch.sparse_coo_tensor,
        torch.sparse_compressed_tensor,
        torch.sparse_csr_tensor,
        torch.sparse_csc_tensor,
        torch.sparse_bsr_tensor,
        torch.sparse_bsc_tensor,
        torch.tril_indices,
        torch.triu_indices,
        torch.vander,
        torch.zeros,
        torch._jit_internal.boolean_dispatch,
        torch.nn.functional.assert_int_or_pair,
        torch.nn.functional.upsample,
        torch.nn.functional.upsample_bilinear,
        torch.nn.functional.upsample_nearest,
        torch.nn.functional.has_torch_function,
        torch.nn.functional.has_torch_function_unary,
        torch.nn.functional.has_torch_function_variadic,
        torch.nn.functional.handle_torch_function,
        torch.nn.functional.sigmoid,
        torch.nn.functional.hardsigmoid,
        torch.nn.functional.tanh,
        # Doesn't actually take or return tensor arguments
        torch.nn.init.calculate_gain,
        # These are deprecated; don't test them
        torch.nn.init.uniform,
        torch.nn.init.normal,
        torch.nn.init.constant,
        torch.nn.init.eye,
        torch.nn.init.dirac,
        torch.nn.init.xavier_uniform,
        torch.nn.init.xavier_normal,
        torch.nn.init.kaiming_uniform,
        torch.nn.init.kaiming_normal,
        torch.nn.init.orthogonal,
        torch.nn.init.sparse,
        has_torch_function,
        handle_torch_function,
        torch.set_autocast_enabled,
        torch.is_autocast_enabled,
        torch.clear_autocast_cache,
        torch.set_autocast_cpu_enabled,
        torch.is_autocast_cpu_enabled,
        torch.set_autocast_cpu_dtype,
        torch.get_autocast_cpu_dtype,
        torch.get_autocast_gpu_dtype,
        torch.set_autocast_gpu_dtype,
        torch.autocast_increment_nesting,
        torch.autocast_decrement_nesting,
        torch.is_autocast_cache_enabled,
        torch.set_autocast_cache_enabled,
        torch.nn.functional.hardswish,
        torch.is_vulkan_available,
        torch.are_deterministic_algorithms_enabled,
        torch.use_deterministic_algorithms,
        torch.is_deterministic_algorithms_warn_only_enabled,
        torch.set_deterministic_debug_mode,
        torch.get_deterministic_debug_mode,
        torch.set_float32_matmul_precision,
        torch.get_float32_matmul_precision,
        torch.unify_type_list,
        torch.is_warn_always_enabled,
        torch.set_warn_always,
        torch.vitals_enabled,
        torch.set_vital,
        torch.read_vitals,
        torch.frombuffer,
        torch.asarray,
        Tensor.__delitem__,
        Tensor.__dir__,
        Tensor.__getattribute__,
        Tensor.__init__,
        Tensor.__iter__,
        Tensor.__init_subclass__,
        Tensor.__delattr__,
        Tensor.__setattr__,
        Tensor.__torch_function__,
        Tensor.__torch_dispatch__,
        Tensor.__new__,
        Tensor.__class__,
        Tensor.__subclasshook__,
        Tensor.as_subclass,
        Tensor.reinforce,
        Tensor.new,
        Tensor.new_tensor,
        Tensor.new_empty,
        Tensor.new_empty_strided,
        Tensor.new_zeros,
        Tensor.new_ones,
        Tensor.new_full,
        Tensor._make_subclass,
        Tensor.solve,
        Tensor.stride,
        Tensor.unflatten,
        Tensor.to_sparse_coo,
        Tensor.to_sparse_csr,
        Tensor.to_sparse_csc,
        Tensor.to_sparse_bsr,
        Tensor.to_sparse_bsc,
        Tensor._reduce_ex_internal,
        Tensor._fix_weakref,
        Tensor._make_wrapper_subclass,
        Tensor._python_dispatch.__get__,
        Tensor._conj,
        Tensor._conj_physical,
        Tensor._neg_view,
        Tensor._is_zerotensor,
        Tensor._addmm_activation,
        Tensor._nested_tensor_layer_norm,
        Tensor.to_padded_tensor,
        Tensor.sym_size
    }


@functools.lru_cache(None)
def get_default_nowrap_functions() -> Set[Callable]:
    """
    Return public functions that do not wrap in a subclass when invoked by
    the default ``Tensor.__torch_function__`` that preserves subclasses.  Typically,
    these functions represent field accesses (i.e., retrieving a Tensor that
    is stored somewhere on the Tensor) as opposed to computation.  Users of
    these functions expect object identity to be preserved over multiple accesses
    (e.g., ``a.grad is a.grad``) which cannot be upheld if we're wrapping on
    the fly every time (furthermore, the tensor stored here might already be
    the subclass, in which case wrapping really ought not to happen).

    Not ALL property accessors have this property; for example ``Tensor.T`` actually
    just creates a new transposed tensor on the fly, and so we SHOULD interpose on
    these calls (you need to check the implementation of the function to see if
    this is the case or not).  Additionally, if a property accessor doesn't return a Tensor,
    it doesn't have to be on this list (though it is harmless if it is).
    """
    Tensor = torch.Tensor
    return {
        Tensor._base.__get__,
        Tensor.grad.__get__,
        Tensor._grad.__get__,
    }


@functools.lru_cache(None)
def get_testing_overrides() -> Dict[Callable, Callable]:
    """Return a dict containing dummy overrides for all overridable functions

    Returns
    -------
    Dict[Callable, Callable]
        A dictionary that maps overridable functions in the PyTorch API to
        lambda functions that have the same signature as the real function
        and unconditionally return -1. These lambda functions are useful
        for testing API coverage for a type that defines ``__torch_function__``.

    Examples
    --------
    >>> import inspect
    >>> my_add = torch.overrides.get_testing_overrides()[torch.add]
    >>> inspect.signature(my_add)
    <Signature (input, other, out=None)>
    """
    # Every function in the PyTorchAPI that can be overriden needs an entry
    # in this dict.
    #
    # Optimally we would use inspect to get the function signature and define
    # the lambda function procedurally but that is blocked by generating
    # function signatures for native kernels that can be consumed by inspect.
    # See Issue #28233.
    Tensor = torch.Tensor
    ret: Dict[Callable, Callable] = {
        torch.abs: lambda input, out=None: -1,
        torch.absolute: lambda input, out=None: -1,
        torch.adaptive_avg_pool1d: lambda input, output_size: -1,
        torch.adaptive_max_pool1d: lambda inputs, output_size: -1,
        torch.acos: lambda input, out=None: -1,
        torch.adjoint: lambda input: -1,
        torch.arccos: lambda input, out=None: -1,
        torch.acosh: lambda input, out=None: -1,
        torch.arccosh: lambda input, out=None: -1,
        torch.add: lambda input, other, out=None: -1,
        torch.addbmm: lambda input, batch1, batch2, alpha=1, beta=1, out=None: -1,
        torch.addcdiv: lambda input, tensor1, tensor2, value=1, out=None: -1,
        torch.addcmul: lambda input, tensor1, tensor2, value=1, out=None: -1,
        torch.addmm: lambda input, mat1, mat2, beta=1, alpha=1, out=None: -1,
        torch.addmv: lambda input, mat, vec, beta=1, alpha=1, out=None: -1,
        torch.addr: lambda input, vec1, vec2, beta=1, alpha=1, out=None: -1,
        torch.affine_grid_generator: lambda theta, size, align_corners: -1,
        torch.all: lambda input, dim=None: -1,
        torch.allclose: lambda input, other, trol=1e-05, atol=1e-08, equal_nan=False: -1,
        torch.alpha_dropout: lambda input, p, train, inplace=False: -1,
        torch.amax: lambda input, dim=None: -1,
        torch.amin: lambda input, dim=None: -1,
        torch.aminmax: lambda input, dim=None, keepdim=False, out=None: -1,
        torch.angle: lambda input, out=None: -1,
        torch.any: lambda input, dim=None, keepdim=False, out=None: -1,
        torch.argmax: lambda input: -1,
        torch.argmin: lambda input: -1,
        torch.argsort: lambda input, dim=None: -1,
        torch.asin: lambda input, out=None: -1,
        torch._assert_async: lambda input: -1,
        torch.arcsin: lambda input, out=None: -1,
        torch.asinh: lambda input, out=None: -1,
        torch.arcsinh: lambda input, out=None: -1,
        torch.atan: lambda input, out=None: -1,
        torch.arctan: lambda input, out=None: -1,
        torch.atan2: lambda input, other, out=None: -1,
        torch.arctan2: lambda input, other, out=None: -1,
        torch.atanh: lambda input, out=None: -1,
        torch.arctanh: lambda input, out=None: -1,
        torch.atleast_1d: lambda *tensors: -1,
        torch.atleast_2d: lambda *tensors: -1,
        torch.atleast_3d: lambda *tensors: -1,
        torch.avg_pool1d: lambda input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True: -1,
        torch.baddbmm: lambda input, batch1, batch2, alpha=1, beta=1, out=None: -1,
        torch.batch_norm: lambda input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled: -1,
        torch.batch_norm_backward_elemt: lambda grad_out, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count_tensor: -1,
        torch.batch_norm_backward_reduce: lambda grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g: -1,
        torch.batch_norm_elemt: lambda input, weight, bias, mean, invstd, eps: -1,
        torch.batch_norm_gather_stats: lambda input, mean, invstd, running_mean, running_var, momentum, eps, count: -1,
        torch.batch_norm_gather_stats_with_counts: lambda input, mean, invstd, running_mean, running_var, momentum, eps, count: -1,
        torch.batch_norm_stats: lambda input, eps: -1,
        torch.batch_norm_update_stats: lambda input, running_mean, running_var, momentum: -1,
        torch.bernoulli: lambda input, generator=None, out=None: -1,
        torch.bilinear: lambda input1, input2, weight, bias: -1,
        torch.binary_cross_entropy_with_logits: (lambda input, target, weight=None, size_average=None, reduce=None,
                                                 reduction='mean', pos_weight=None: -1),
        torch.bincount: lambda input, weights=None, minlength=0: -1,
        torch.binomial: lambda count, prob, generator=None: -1,
        torch.bitwise_and: lambda input, other, out=None: -1,
        torch.bitwise_not: lambda input, out=None: -1,
        torch.bitwise_or: lambda input, other, out=None: -1,
        torch.bitwise_xor: lambda input, other, out=None: -1,
        torch.bitwise_left_shift: lambda input, other, out=None: -1,
        torch.bitwise_right_shift: lambda input, other, out=None: -1,
        torch.block_diag: lambda *tensors: -1,
        torch.bmm: lambda input, mat2, out=None: -1,
        torch.broadcast_tensors: lambda *tensors: -1,
        torch.broadcast_to: lambda self, size: -1,
        torch.bucketize: lambda input, boundaries, out_int32=False, right=False, out=None: -1,
        torch.cartesian_prod: lambda *tensors: -1,
        torch.cat: lambda tensors, dim=0, out=None: -1,
        torch.concat: lambda tensors, dim=0, out=None: -1,  # alias for torch.cat
        torch.cdist: lambda x1, x2, p=2.0, compute_mode='use_mm_for_euclid_dist_if_necessary': -1,
        torch.ceil: lambda input, out=None: -1,
        torch.celu: lambda input, alhpa=1., inplace=False: -1,
        torch.chain_matmul: lambda *matrices, out=None: -1,
        torch.channel_shuffle: lambda input, groups : -1,
        torch.cholesky: lambda input, upper=False, out=None: -1,
        torch.linalg.cholesky: lambda input, out=None: -1,
        torch.linalg.cholesky_ex: lambda input, check_errors=False, out=None: -1,
        torch.cholesky_inverse: lambda input, upper=False, out=None: -1,
        torch.cholesky_solve: lambda input1, input2, upper=False, out=None: -1,
        torch.choose_qparams_optimized: lambda input, numel, n_bins, ratio, bit_width: -1,
        torch.chunk: lambda input, chunks, dim=0: -1,
        torch.clamp: lambda input, min=None, max=None, out=None: -1,
        torch.clip: lambda input, min=None, max=None, out=None: -1,
        torch.clamp_min: lambda input, min, out=None: -1,
        torch.clamp_max: lambda input, max, out=None: -1,
        torch.column_stack: lambda tensors, out=None: -1,
        torch.cov: lambda input, correction=1, fweights=None, aweights=None: -1,
        torch.clone: lambda input: -1,
        torch.combinations: lambda input, r=2, with_replacement=False: -1,
        torch.complex: lambda real, imag: -1,
        torch.copysign: lambda input, other, out=None: -1,
        torch.polar: lambda abs, ang: -1,
        torch.linalg.cond: lambda input, ord=None: -1,
        torch.conj: lambda input, out=None: -1,
        torch.conj_physical: lambda input, out=None: -1,
        torch.resolve_conj: lambda input, out=None: -1,
        torch.resolve_neg: lambda input, out=None: -1,
        torch.constant_pad_nd: lambda input, pad, value=0: -1,
        torch.conv1d: lambda input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1: -1,
        torch.conv2d: lambda input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1: -1,
        torch.conv3d: lambda input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1: -1,
        torch.convolution: lambda input, weight, bias, stride, padding, dilation, transposed, output_adding, groups: -1,
        torch.conv_tbc: lambda input, weight, bias, pad=0: -1,
        torch.conv_transpose1d: lambda input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1: -1,
        torch.conv_transpose2d: lambda input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1: -1,
        torch.conv_transpose3d: lambda input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1: -1,
        torch.corrcoef: lambda input: -1,
        torch.cos: lambda input, out=None: -1,
        torch.cosine_embedding_loss: lambda input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean': -1,
        torch.cosh: lambda input, out=None: -1,
        torch.cosine_similarity: lambda x1, x2, dim=1, eps=1e-8: -1,
        torch.count_nonzero: lambda input: -1,
        torch.cross: lambda input, other, dim=None, out=None: -1,
        torch.linalg.cross: lambda input, other, dim=-1, out=None: -1,
        torch.ctc_loss: (lambda log_probs, targets, input_lengths, target_lengths, blank=0, reduction='mean',
                         zero_infinity=False: -1),
        torch.cummax: lambda input, dim, out=None: -1,
        torch.cummin: lambda input, dim, out=None: -1,
        torch.cumprod: lambda input, dim, out=None, dtype=None: -1,
        torch.cumsum: lambda input, dim, out=None, dtype=None: -1,
        torch.cumulative_trapezoid: lambda y, x=None, dim=-1: -1,
        torch.logcumsumexp: lambda input, dim, out=None: -1,
        torch.deg2rad: lambda input, out=None: -1,
        torch.dequantize: lambda input: -1,
        torch.det: lambda input: -1,
        torch.linalg.det: lambda input: -1,  # alias for torch.det  # type: ignore[attr-defined]
        torch.detach: lambda input: -1,
        torch.diag: lambda input, diagonal=0, out=None: -1,
        torch.diag_embed: lambda input, diagonal=0, out=None: -1,
        torch.diagflat: lambda input, offset=0: -1,
        torch.diff: lambda input, n=1, dim=-1, prepend=None, append=None, out=None: -1,
        torch.diagonal: lambda input, offset=0, dim1=0, dim2=1: -1,
        torch.linalg.diagonal: lambda input, offset=0, dim1=-2, dim2=-1: -1,
        torch.diagonal_scatter: lambda input, src, offset=0, dim1=0, dim2=1: -1,
        torch.as_strided_scatter: lambda self, src, size, stride, storage_offset=None: -1,
        torch.digamma: lambda input, out=None: -1,
        torch.dist: lambda input, other, p=2: -1,
        torch.div: lambda input, other, rounding_mode=None, out=None: -1,
        torch.divide: lambda input, other, rounding_mode=None, out=None: -1,
        torch.dot: lambda input, other, out=None: -1,
        torch.dropout: lambda input, p, train, inplace=False: -1,
        torch.dsmm: lambda input, mat2: -1,
        torch.hsmm: lambda mat1, mat2: -1,
        torch.dsplit: lambda input, indices_or_sections: -1,
        torch.dstack: lambda tensors, out=None: -1,
        torch.eig: lambda input, eigenvectors=False, out=None: -1,
        torch.linalg.eig: lambda input, out=None: -1,
        torch.linalg.eigvals: lambda input, out=None: -1,
        torch.linalg.eigh: lambda input, UPLO="L", out=None: -1,
        torch.linalg.eigvalsh: lambda input, UPLO="L", out=None: -1,
        torch.einsum: lambda equation, *operands: -1,
        torch.embedding: (lambda input, weight, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False,
                          sparse=False: -1),
        torch.embedding_bag: (lambda input, weight, offsets, max_norm=None, norm_type=2, scale_grad_by_freq=False,
                              mode='mean', sparse=False, per_sample_weights=None, padding_idx=None: -1),
        torch.empty_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
        torch.eq: lambda input, other, out=None: -1,
        torch.equal: lambda input, other: -1,
        torch.erf: lambda input, out=None: -1,
        torch.erfc: lambda input, out=None: -1,
        torch.erfinv: lambda input, out=None: -1,
        torch.exp: lambda input, out=None: -1,
        torch.exp2: lambda input, out=None: -1,
        torch.expm1: lambda input, out=None: -1,
        torch.fake_quantize_per_channel_affine: lambda input, scale, zero_point, axis, quant_min, quant_max: -1,
        torch.fake_quantize_per_tensor_affine: lambda input, scale, zero_point, quant_min, quant_max: -1,
        torch.fused_moving_avg_obs_fake_quant: (lambda x, observer_on, fake_quant_on, averaging_const, running_min,
                                                running_max, scale, zero_point, quant_min, quant_max, ch_axis,
                                                per_row_fake_quant=False, symmetric_quant=False: -1),
        torch.fbgemm_linear_fp16_weight: lambda input, packed_weight, bias: -1,
        torch.fbgemm_linear_fp16_weight_fp32_activation: lambda input, packed_weight, bias: -1,
        torch.fbgemm_linear_int8_weight: lambda input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias: -1,
        torch.fbgemm_linear_int8_weight_fp32_activation: (lambda input, weight, packed, col_offsets, weight_scale,
                                                          weight_zero_point, bias: -1),
        torch.fbgemm_linear_quantize_weight: lambda input: -1,
        torch.fbgemm_pack_gemm_matrix_fp16: lambda input: -1,
        torch.fbgemm_pack_quantized_matrix: lambda input, a, b: -1,
        torch.feature_alpha_dropout: lambda input, p, train: -1,
        torch.feature_dropout: lambda input, p, train: -1,
        torch.fft.fft: lambda input, n=None, dim=-1, norm=None: -1,
        torch.fft.ifft: lambda input, n=None, dim=-1, norm=None: -1,
        torch.fft.rfft: lambda input, n=None, dim=-1, norm=None: -1,
        torch.fft.irfft: lambda input, n=None, dim=-1, norm=None: -1,
        torch.fft.hfft: lambda input, n=None, dim=-1, norm=None: -1,
        torch.fft.ihfft: lambda input, n=None, dim=-1, norm=None: -1,
        torch.fft.hfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
        torch.fft.ihfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
        torch.fft.hfftn: lambda input, s=None, dim=-1, norm=None: -1,
        torch.fft.ihfftn: lambda input, s=None, dim=-1, norm=None: -1,
        torch.fft.fftn: lambda input, s=None, dim=None, norm=None: -1,
        torch.fft.ifftn: lambda input, s=None, dim=None, norm=None: -1,
        torch.fft.rfftn: lambda input, s=None, dim=None, norm=None: -1,
        torch.fft.irfftn: lambda input, s=None, dim=None, norm=None: -1,
        torch.fft.fft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
        torch.fft.ifft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
        torch.fft.rfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
        torch.fft.irfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
        torch.fft.fftshift: lambda input, dim=None: -1,
        torch.fft.ifftshift: lambda input, dim=None: -1,
        torch.fft.fft: lambda input, n=None, dim=-1, norm=None: -1,
        torch.fix: lambda input, out=None: -1,
        torch.flatten: lambda input, start_dim=0, end_dim=-1: -1,
        torch.flip: lambda input, dims: -1,
        torch.fliplr: lambda input: -1,
        torch.flipud: lambda input: -1,
        torch.frobenius_norm: lambda input, dim=None, keepdim=False, out=None: -1,
        torch.floor: lambda input, out=None: -1,
        torch.floor_divide: lambda input, other: -1,
        torch.float_power: lambda input, exponent, out=None: -1,
        torch.fmod: lambda input, other, out=None: -1,
        torch.frac: lambda input, out=None: -1,
        torch.frexp: lambda input, out=None: -1,
        torch.full_like: lambda input, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False: -1,
        torch.lu_unpack: lambda LU_data, LU_pivots, unpack_data=True, unpack_pivots=True: -1,
        torch.gather: lambda input, dim, index, out=None, sparse_grad=False: -1,
        torch.gcd: lambda input, other, out=None: -1,
        torch.ge: lambda input, other, out=None: -1,
        torch.greater_equal: lambda input, other, out=None: -1,
        torch.geqrf: lambda input, out=None: -1,
        torch.i0: lambda input, out=None: -1,
        torch.inner: lambda input, other, out=None: -1,
        torch.outer: lambda input, vec2, out=None: -1,
        torch.ger: lambda input, vec2, out=None: -1,  # alias for torch.outer
        torch.gradient: lambda input, spacing=None, dim=None, edge_order=1: -1,
        torch.grid_sampler: lambda input, grid, interpolation_mode, padding_mode, align_corners: -1,
        torch.grid_sampler_2d: lambda input, grid, interpolation_mode, padding_mode, align_corners: -1,
        torch.grid_sampler_3d: lambda input, grid, interpolation_mode, padding_mode, align_corners: -1,
        torch.group_norm: lambda input, num_groups, weight=None, bias=None, eps=1e-05, cudnn_enabled=True: -1,
        torch.gru: lambda input, hx, params, has_biases, num_layers, gropout, train, bidirectional, batch_first: -1,
        torch.gru_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
        torch.gt: lambda input, other, out=None: -1,
        torch.greater: lambda input, other, out=None: -1,
        torch.hardshrink: lambda input, lambd=0.5: -1,
        torch.heaviside: lambda input, values, out=None: -1,
        torch.hinge_embedding_loss: lambda input, target, margin=1.0, size_average=None, reduce=None, reduction='mean': -1,
        torch.histc: lambda input, bins=100, min=0, max=0, out=None: -1,
        torch.histogram: lambda input, bins=100, min=None, max=None, weight=None, density=False, out=None: -1,
        torch.histogramdd: lambda input, bins, range=None, weight=None, density=False: -1,
        torch.linalg.householder_product: lambda input, tau: -1,
        torch.hspmm: lambda mat1, mat2, out=None: -1,
        torch.hsplit: lambda input, indices_or_sections: -1,
        torch.hstack: lambda tensors, out=None: -1,
        torch.hypot: lambda input, other, out=None: -1,
        torch.igamma: lambda input, other, out=None: -1,
        torch.igammac: lambda input, other, out=None: -1,
        torch.imag: lambda input, out=None: -1,
        torch.index_add: lambda input, dim, index, source: -1,
        torch.index_copy: lambda input, dim, index, source: -1,
        torch.index_put: lambda input, indices, values, accumulate=False: -1,
        torch.index_select: lambda input, dim, index, out=None: -1,
        torch.index_fill: lambda input, dim, index, value: -1,
        torch.index_reduce: lambda input, dim, index, source, reduce, include_input=True: -1,
        torch.isfinite: lambda tensor: -1,
        torch.isin: lambda e, te, assume_unique=False, invert=False: -1,
        torch.isinf: lambda tensor: -1,
        torch.isreal: lambda tensor: -1,
        torch.isposinf: lambda input, out=None: -1,
        torch.isneginf: lambda input, out=None: -1,
        torch.instance_norm: (lambda input, running_mean, running_var, weight, bias, use_input_stats, momentum, eps,
                              cudnn_enabled: -1),
        torch.int_repr: lambda input: -1,
        torch.inverse: lambda input, out=None: -1,
        torch.linalg.inv: lambda input, out=None: -1,
        torch.linalg.inv_ex: lambda input, check_errors=False, out=None: -1,
        torch.is_complex: lambda input: -1,
        torch.is_conj: lambda input: -1,
        torch.is_neg: lambda input: -1,
        torch.is_distributed: lambda input: -1,
        torch.is_inference: lambda input: -1,
        torch.is_floating_point: lambda input: -1,
        torch.is_nonzero: lambda input: -1,
        torch.is_same_size: lambda input, other: -1,
        torch.is_signed: lambda input: -1,
        torch.isclose: lambda input, other, rtol=1e-05, atol=1e-08, equal_nan=False: -1,
        torch.isnan: lambda input: -1,
        torch.istft: (lambda input, n_fft, hop_length=None, win_length=None, window=None, center=True,
                      normalized=False, onesided=None, length=None, return_complex=False: -1),
        torch.kl_div: lambda input, target, size_average=None, reduce=None, reduction='mean', log_target=False: -1,
        torch.kron: lambda input, other: -1,
        torch.kthvalue: lambda input, k, dim=None, keepdim=False, out=None: -1,
        torch.linalg.ldl_factor_ex: lambda input, hermitian=False, check_errors=False, out=None: -1,
        torch.linalg.ldl_factor: lambda input, hermitian=False, out=None: -1,
        torch.linalg.ldl_solve: lambda LD, pivots, B, hermitian=False, out=None: -1,
        torch.layer_norm: lambda input, normalized_shape, weight=None, bias=None, esp=1e-05, cudnn_enabled=True: -1,
        torch.lcm: lambda input, other, out=None: -1,
        torch.ldexp: lambda input, other, out=None: -1,
        torch.le: lambda input, other, out=None: -1,
        torch.less_equal: lambda input, other, out=None: -1,
        torch.lerp: lambda input, end, weight, out=None: -1,
        torch.lgamma: lambda input, out=None: -1,
        torch.lobpcg: lambda input, k=None, B=None, X=None, n=None, iK=None, niter=None, tol=None, largest=None, method=None,
        tracker=None, ortho_iparams=None, ortho_fparams=None, ortho_bparams=None: -1,
        torch.log: lambda input, out=None: -1,
        torch.log_softmax: lambda input, dim, dtype=None: -1,
        torch.log10: lambda input, out=None: -1,
        torch.log1p: lambda input, out=None: -1,
        torch.log2: lambda input, out=None: -1,
        torch.logaddexp: lambda input, other, out=None: -1,
        torch.logaddexp2: lambda input, other, out=None: -1,
        torch.logdet: lambda input: -1,
        torch.xlogy: lambda x, y, out=None: -1,
        torch.logical_and: lambda input, other, out=None: -1,
        torch.logical_not: lambda input, out=None: -1,
        torch.logical_or: lambda input, other, out=None: -1,
        torch.logical_xor: lambda input, other, out=None: -1,
        torch.logsumexp: lambda input, names, keepdim=False, out=None: -1,
        torch.logit: lambda input, eps=None: -1,
        torch.logsumexp: lambda input, names, keepdim=False, out=None: -1,
        torch.lstm: lambda data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional: -1,
        torch.lstm_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
        torch.lstsq: lambda input, A, out=None: -1,
        torch.lt: lambda input, other, out=None: -1,
        torch.less: lambda input, other, out=None: -1,
        torch.lu: lambda A, pivot=True, get_infos=False, out=None: -1,
        torch.lu_solve: lambda b, LU_data, LU_pivots, out=None: -1,
        torch.margin_ranking_loss: lambda input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean': -1,  # type: ignore[attr-defined]  # noqa: B950
        torch.masked_fill: lambda input, mask, value: -1,
        torch.masked_scatter: lambda input, mask, source: -1,
        torch.masked_select: lambda input, mask, out=None: -1,
        torch.matmul: lambda input, other, out=None: -1,
        torch.linalg.lu: lambda input, pivot=True, out=None: -1,
        torch.linalg.lu_factor: lambda input, pivot=True, out=None: -1,
        torch.linalg.lu_factor_ex: lambda input, pivot=True, check_errors=False, out=None: -1,
        torch.linalg.lu_solve: lambda LU, pivots, B, left=True, adjoint=False, out=None: -1,
        torch.linalg.matmul: lambda input, other, out=None: -1,  # alias for torch.matmul
        torch.matrix_power: lambda input, n: -1,
        torch.linalg.matrix_power: lambda input, n, out=None: -1,
        torch.matrix_rank: lambda input, tol=None, symmetric=False: -1,
        torch.linalg.matrix_rank: lambda input, tol=None, hermitian=False: -1,
        torch.linalg.multi_dot: lambda tensors, out=None: -1,
        torch.matrix_exp: lambda input: -1,
        torch.linalg.matrix_exp: lambda input: -1,
        torch.max: lambda input, out=None: -1,
        torch.maximum: lambda input, other, out=None: -1,
        torch.fmax: lambda input, other, out=None: -1,
        torch.max_pool1d: lambda input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False: -1,
        torch.max_pool2d: lambda input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False: -1,
        torch.max_pool3d: lambda input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False: -1,
        torch.max_pool1d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
                                        return_indices=False, ceil_mode=False: -1),
        torch.mean: lambda input, dim=None: -1,
        torch.nanmean: lambda input, dim=None, keepdim=False, dtype=None, out=None: -1,
        torch.median: lambda input, dim=None: -1,
        torch.nanmedian: lambda input, dim=None: -1,
        torch.meshgrid: lambda *tensors, **kwargs: -1,
        torch.min: lambda input, out=None: -1,
        torch.minimum: lambda input, other, out=None: -1,
        torch.fmin: lambda input, other, out=None: -1,
        torch.miopen_batch_norm: (lambda input, weight, bias, running_mean, running_var, training,
                                  exponential_average_factor, epsilon: -1),
        torch.miopen_convolution: lambda input, weight, bias, padding, stride, dilation, groups, benchmark, deterministic: -1,
        torch.miopen_convolution_transpose: (lambda input, weight, bias, padding, output_padding, stride, dilation,
                                             groups, benchmark, deterministic: -1),
        torch.miopen_depthwise_convolution: (lambda input, weight, bias, padding, stride, dilation, groups, benchmark,
                                             deterministic: -1),
        torch.miopen_rnn: (lambda input, weight, weight_stride0, hx, cx, mode, hidden_size, num_layers, batch_first,
                           dropout, train, bidirectional, batch_sizes, dropout_state: -1),
        torch.mm: lambda input, mat2, out=None: -1,
        torch.mode: lambda input, dim=-1, keepdim=False, out=None: -1,
        torch.movedim: lambda input, source, destination: -1,
        torch.moveaxis: lambda input, source, destination: -1,
        torch.msort: lambda input, descending=False, out=None: -1,
        torch.mul: lambda input, other, out=None: -1,
        torch.multiply: lambda input, other, out=None: -1,
        torch.multinomial: lambda input, num_samples, replacement=False, out=None: -1,
        torch.mv: lambda input, vec, out=None: -1,
        torch.mvlgamma: lambda input, p: -1,
        torch.narrow: lambda input, dim, start, length: -1,
        torch.narrow_copy: lambda input, dim, start, length: -1,
        torch.nan_to_num: lambda input, nan=0.0, posinf=None, neginf=None, out=None: -1,
        torch.native_batch_norm: lambda input, weight, bias, running_mean, running_var, training, momentum, eps: -1,
        torch.native_dropout: lambda input, p, train: -1,
        torch.native_layer_norm: lambda input, normalized_shape, weight=None, bias=None, eps=1e-05: -1,
        torch.native_group_norm: lambda input, weight, bias, N, C, HxW, group, eps: -1,
        torch.native_norm: lambda input, p=2: -1,
        torch.native_norm: lambda input, p=2: -1,
        torch.native_norm: lambda input, p=2, dim=None, keepdim=False, dtype=None: -1,
        torch.native_channel_shuffle: lambda input, groups : -1,
        torch.ne: lambda input, other, out=None: -1,
        torch.not_equal: lambda input, other, out=None: -1,
        torch.neg: lambda input, out=None: -1,
        torch.negative: lambda input, out=None: -1,
        torch.nextafter: lambda input, other, out=None: -1,
        torch.nn.functional.adaptive_avg_pool2d: lambda input, output_size: -1,
        torch.nn.functional.adaptive_avg_pool3d: lambda input, output_size: -1,
        torch.nn.functional.adaptive_max_pool1d: lambda input, output_size, return_indices=False: -1,
        torch.nn.functional.adaptive_max_pool1d_with_indices: lambda input, output_size, return_indices=False: -1,
        torch.nn.functional.adaptive_max_pool2d: lambda input, output_size, return_indices=False: -1,
        torch.nn.functional.adaptive_max_pool2d_with_indices: lambda input, output_size, return_indices=False: -1,
        torch.nn.functional.adaptive_max_pool3d: lambda input, output_size, return_indices=False: -1,
        torch.nn.functional.adaptive_max_pool3d_with_indices: lambda input, output_size, return_indices=False: -1,
        torch.nn.functional.affine_grid: lambda theta, size, align_corners=None: -1,
        torch.nn.functional.alpha_dropout: lambda input, p=0.5, training=False, inplace=False: -1,
        torch.nn.functional.avg_pool2d: (lambda input, kernel_size, stride=None, padding=0, ceil_mode=False,
                                         count_include_pad=True, divisor_override=None: -1),
        torch.nn.functional.avg_pool3d: (lambda input, kernel_size, stride=None, padding=0, ceil_mode=False,
                                         count_include_pad=True, divisor_override=None: -1),
        torch.nn.functional.batch_norm: (lambda input, running_mean, running_var, weight=None, bias=None, training=False,
                                         momentum=0.1, eps=1e-05: -1),
        torch.nn.functional.bilinear: lambda input1, input2, weight, bias=None: -1,
        torch.nn.functional.binary_cross_entropy: (lambda input, target, weight=None, size_average=None, reduce=None,
                                                   reduction="mean": -1),
        torch.nn.functional.binary_cross_entropy_with_logits: (lambda input, target, weight=None, size_average=None,
                                                               reduce=None, reduction="mean", pos_weight=None: -1),
        torch.nn.functional.celu: lambda input, alpha=1.0, inplace=False: -1,
        torch.nn.functional.cosine_embedding_loss: (lambda input1, input2, target, margin=0, size_average=None,
                                                    reduce=None, reduction='mean': -1),
        torch.nn.functional.cross_entropy: (lambda input, target, weight=None, size_average=None, ignore_index=-100,
                                            reduce=None, reduction="mean", label_smoothing=0.0: -1),
        torch.nn.functional.ctc_loss: (lambda log_probs, targets, input_lengths, target_lengths, blank=0,
                                       reduction='mean', zero_infinity=False: -1),
        torch.nn.functional.dropout: lambda input, p=0.5, training=True, inplace=False: -1,
        torch.nn.functional.dropout1d: lambda input, p=0.5, training=True, inplace=False: -1,
        torch.nn.functional.dropout2d: lambda input, p=0.5, training=True, inplace=False: -1,
        torch.nn.functional.dropout3d: lambda input, p=0.5, training=True, inplace=False: -1,
        torch.nn.functional.elu: lambda input, alpha=1.0, inplace=False: -1,
        torch.nn.functional.embedding: (lambda input, weight, padding_idx=None, max_norm=None, norm_type=2.0,
                                        scale_grad_by_freq=False, sparse=False: -1),
        torch.nn.functional.embedding_bag: (lambda input, weight, offsets=None, max_norm=None, norm_type=2,
                                            scale_grad_by_freq=False, mode='mean', sparse=False, per_sample_weights=None,
                                            include_last_offset=False, padding_idx=None: -1),
        torch.nn.functional.feature_alpha_dropout: lambda input, p=0.5, training=False, inplace=False: -1,
        torch.nn.functional.fold: lambda input, output_size, kernel_size, dilation=1, padding=0, stride=1: -1,
        torch.nn.functional.fractional_max_pool2d: (lambda input, kernel_size, output_size=None, output_ratio=None,
                                                    return_indices=False, _random_samples=None: -1),
        torch.nn.functional.fractional_max_pool2d_with_indices: (
            lambda input, kernel_size, output_size=None, output_ratio=None, return_indices=False,
            _random_samples=None: -1),
        torch.nn.functional.fractional_max_pool3d: (lambda input, kernel_size, output_size=None, output_ratio=None,
                                                    return_indices=False, _random_samples=None: -1),
        torch.nn.functional.fractional_max_pool3d_with_indices: (
            lambda input, kernel_size, output_size=None, output_ratio=None, return_indices=False,
            _random_samples=None: -1),
        torch.nn.functional.gaussian_nll_loss: lambda input, target, var, full=False, eps=1e-06, reduction='mean': -1,
        torch.nn.functional.gelu: lambda input, approximate='none': -1,
        torch.nn.functional.glu: lambda input, dim=-1: -1,
        torch.nn.functional.grid_sample: lambda input, grid, mode='bilinear', padding_mode='zeros', align_corners=None: -1,
        torch.nn.functional.group_norm: lambda input, num_groups, weight=None, bias=None, eps=1e-05: -1,
        torch.nn.functional.gumbel_softmax: lambda logits, tau=1, hard=False, eps=1e-10, dim=-1: -1,
        torch.nn.functional.hardshrink: lambda input, lambd=0.5: -1,
        torch.nn.functional.hardtanh: lambda input, min_val=-1., max_val=1., inplace=False: -1,
        torch.nn.functional.hinge_embedding_loss: (lambda input, target, margin=1.0, size_average=None, reduce=None,
                                                   reduction='mean': -1),
        torch.nn.functional.instance_norm: (lambda input, running_mean=None, running_var=None, weight=None, bias=None,
                                            use_input_stats=True, momentum=0.1, eps=1e-05: -1),
        torch.nn.functional.interpolate: (lambda input, size=None, scale_factor=None, mode='nearest', align_corners=None,
                                          recompute_scale_factor=None, antialias=False: -1),
        torch.nn.functional.kl_div: lambda input, target, size_average=None, reduce=None, reduction='mean', log_target=False: -1,
        torch.nn.functional.l1_loss: lambda input, target, size_average=None, reduce=None, reduction='mean': -1,
        torch.nn.functional.layer_norm: lambda input, normalized_shape, weight=None, bias=None, eps=1e-05: -1,
        torch.nn.functional.leaky_relu: lambda input, negative_slope=0.01, inplace=False: -1,
        torch.nn.functional.linear: lambda input, weight, bias=None: -1,
        torch.nn.functional.local_response_norm: lambda input, size, alpha=0.0001, beta=0.75, k=1.0: -1,
        torch.nn.functional.log_softmax: lambda input, dim=None, _stacklevel=3, dtype=None: -1,
        torch.nn.functional.logsigmoid: lambda input: -1,
        torch.nn.functional.lp_pool1d: lambda input, norm_type, kernel_size, stride=None, ceil_mode=False: -1,
        torch.nn.functional.lp_pool2d: lambda input, norm_type, kernel_size, stride=None, ceil_mode=False: -1,
        torch.nn.functional.margin_ranking_loss: (lambda input1, input2, target, margin=0, size_average=None,
                                                  reduce=None, reduction='mean': -1),
        torch.nn.functional.max_pool1d: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
                                         ceil_mode=False, return_indices=False: -1),
        torch.nn.functional.max_pool1d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
                                                      return_indices=False, ceil_mode=False: -1),
        torch.nn.functional.max_pool2d: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
                                         ceil_mode=False, return_indices=False: -1),
        torch.nn.functional.max_pool2d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
                                                      return_indices=False, ceil_mode=False: -1),
        torch.nn.functional.max_pool3d: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
                                         return_indices=False, ceil_mode=False: -1),
        torch.nn.functional.max_pool3d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
                                                      return_indices=False, ceil_mode=False: -1),
        torch.nn.functional.max_unpool1d: lambda input, indices, kernel_size, stride=None, padding=0, output_size=None: -1,
        torch.nn.functional.max_unpool2d: lambda input, indices, kernel_size, stride=None, padding=0, output_size=None: -1,
        torch.nn.functional.max_unpool3d: lambda input, indices, kernel_size, stride=None, padding=0, output_size=None: -1,
        torch.nn.functional.mse_loss: lambda input, target, size_average=None, reduce=None, reduction='mean': -1,
        torch.nn.functional.multi_head_attention_forward: (
            lambda query, key, value, embed_dim_to_check, num_heads, in_proj_weight, in_proj_bias, bias_k, bias_v,
            add_zero_attn, dropout_p, out_proj_weight, out_proj_bias, training=True, key_padding_mask=None,
            need_weights=True, attn_mask=None, use_separate_proj_weight=False, q_proj_weight=None, k_proj_weight=None,
            v_proj_weight=None, static_k=None, static_v=None, average_attn_weights=None: -1),
        torch.nn.functional.multi_margin_loss: (lambda input, target, p=1, margin=1.0, weight=None, size_average=None,
                                                reduce=None, reduction='mean': -1),
        torch.nn.functional.multilabel_margin_loss: (lambda input, target, size_average=None, reduce=None,
                                                     reduction='mean': -1),
        torch.nn.functional.multilabel_soft_margin_loss: (lambda input, target, weight=None, size_average=None,
                                                          reduce=None, reduction='mean': -1),
        torch.nn.functional.nll_loss: (lambda input, target, weight=None, size_average=None, ignore_index=-100,
                                       reduce=None, reduction='mean': -1),
        torch.nn.functional.normalize: lambda input, p=2, dim=1, eps=1e-12, out=None: -1,
        torch.nn.functional.one_hot: lambda tensor, num_classes=-1: -1,
        torch.nn.functional.pad: lambda input, pad, mode='constant', value=0: -1,
        torch.nn.functional.pairwise_distance: lambda x1, x2, p=2.0, eps=1e-06, keepdim=False: -1,
        torch.nn.functional.poisson_nll_loss: (lambda input, target, log_input=True, full=False, size_average=None,
                                               eps=1e-08, reduce=None, reduction='mean': -1),
        torch.nn.functional.prelu: lambda input, weight: -1,
        torch.nn.functional.relu: lambda input, inplace=False: -1,
        torch.nn.functional.relu6: lambda input, inplace=False: -1,
        torch.nn.functional.rrelu: lambda input, lower=0.125, upper=0.3333333333333333, training=False, inplace=False: -1,
        torch.nn.functional.selu: lambda input, inplace=False: -1,
        torch.nn.functional.silu: lambda input, inplace=False: -1,
        torch.nn.functional.mish: lambda input, inplace=False: -1,
        torch.nn.functional.smooth_l1_loss: lambda input, target, size_average=None, reduce=None, reduction='mean', beta=1.: -1,
        torch.nn.functional.huber_loss: lambda input, target, reduction='mean', delta=1.: -1,
        torch.nn.functional.soft_margin_loss: lambda input, target, size_average=None, reduce=None, reduction='mean': -1,
        torch.nn.functional.softmax: lambda input, dim=None, _stacklevel=3, dtype=None: -1,
        torch.nn.functional.softmin: lambda input, dim=None, _stacklevel=3, dtype=None: -1,
        torch.nn.functional.softplus: lambda input, beta=1, threshold=20: -1,
        torch.nn.functional.softshrink: lambda input, lambd=0.5: -1,
        torch.nn.functional.softsign: lambda input: -1,
        torch.nn.functional.tanhshrink: lambda input: -1,
        torch.nn.functional.threshold: lambda input, threshold, value, inplace=False: -1,
        torch.nn.functional.triplet_margin_loss: (lambda anchor, positive, negative, margin=1.0, p=2, eps=1e-06,
                                                  swap=False, size_average=None, reduce=None, reduction='mean': -1),
        torch.nn.functional.triplet_margin_with_distance_loss: (lambda anchor, positive, negative, *,
                                                                distance_function=None, margin=1.0,
                                                                swap=False, reduction='mean': -1),
        torch.nn.functional.unfold: lambda input, kernel_size, dilation=1, padding=0, stride=1: -1,
        torch.nn.init.uniform_: lambda tensor, a=0., b=1.: -1,
        torch.nn.init.constant_: lambda tensor, val: -1,
        torch.nn.init.normal_: lambda tensor, mean=0., std=1.: -1,
        torch.nn.init.constant_: lambda tensor, val: -1,
        torch.nn.init.kaiming_uniform_: lambda tensor, a=0, mode='fan_in', nonlinearity='leaky_relu': -1,
        torch.nonzero: lambda input, as_tuple=False: -1,
        torch.argwhere: lambda input: -1,
        torch.norm: lambda input, p='fro', dim=None, keepdim=False, out=None, dtype=None: -1,
        torch.linalg.norm: lambda input, ord=None, dim=None, keepdim=False, out=None, dtype=None: -1,
        torch.linalg.vector_norm: lambda input, ord=2, dim=None, keepdim=False, out=None, dtype=None: -1,
        torch.linalg.matrix_norm: lambda input, ord='fro', dim=(-2, -1), keepdim=False, out=None, dtype=None: -1,
        torch.norm_except_dim: lambda v, pow=2, dim=0: -1,
        torch.nuclear_norm: lambda input, p='fro', dim=None, keepdim=False, out=None, dtype=None: -1,
        torch.numel: lambda input: -1,
        torch.orgqr: lambda input, tau: -1,
        torch.ormqr: lambda input, input2, input3, left=True, transpose=False: -1,
        torch.pairwise_distance: lambda x1, x2, p=2.0, eps=1e-06, keepdim=False: -1,
        torch.permute: lambda self, dim: -1,
        torch.pca_lowrank: lambda input, q=None, center=True, niter=2: -1,
        torch.pdist: lambda input, p=2: -1,
        torch.pinverse: lambda input, rcond=1e-15: -1,
        torch.linalg.pinv: lambda input, rcond=1e-15, hermitian=False: -1,
        torch.pixel_shuffle: lambda input, upscale_factor: -1,
        torch.pixel_unshuffle: lambda input, downscale_factor: -1,
        torch.poisson: lambda input, generator=None: -1,
        torch.poisson_nll_loss: lambda input, target, log_input, full, eps, reduction: -1,
        torch.polygamma: lambda input, n, out=None: -1,
        torch.positive: lambda input, out=None: -1,
        torch.prelu: lambda input, weight: -1,
        torch.ones_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
        torch.pow: lambda input, exponent, out=None: -1,
        torch.prod: lambda input, dtype=None: -1,
        torch.put: lambda input, index, source, accumulate=False: -1,
        torch.q_per_channel_axis: lambda input: -1,
        torch.q_per_channel_scales: lambda input: -1,
        torch.q_per_channel_zero_points: lambda input: -1,
        torch.q_scale: lambda input: -1,
        torch.q_zero_point: lambda input: -1,
        torch.qr: lambda input, some=True, out=None: -1,
        torch.linalg.qr: lambda input, mode='reduced', out=None: -1,
        torch.quantile: lambda input, q, dim=None, keepdim=False, interpolation='linear', out=None: -1,
        torch.nanquantile: lambda input, q, dim=None, keepdim=False, interpolation='linear', out=None: -1,
        torch.quantize_per_channel: lambda input, scales, zero_points, axis, dtype: -1,
        torch.quantize_per_tensor: lambda input, scale, zero_point, dtype: -1,
        torch.quantize_per_tensor_dynamic: lambda input, dtype, reduce_range: -1,
        torch.quantized_batch_norm: lambda input, weight, bias, mean, var, eps, output_scale, output_zero_point: -1,
        torch.quantized_gru_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih,
                                   col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1),

        torch.quantized_lstm_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih,
                                    col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1),
        torch.quantized_max_pool1d: (lambda input, kernel_size, stride=tuple(), padding=(0,),
                                     dilation=(1,), ceil_mode=False: -1),
        torch.quantized_max_pool2d: (lambda input, kernel_size, stride=tuple(), padding=(0, 0),
                                     dilation=(1, 1), ceil_mode=False: -1),
        torch.quantized_rnn_relu_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih,
                                        col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1),
        torch.quantized_rnn_tanh_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih,
                                        col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1),
        torch.rad2deg: lambda input, out=None: -1,
        torch.rand_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
        torch.randint_like: lambda input, high, dtype=None, layout=torch.strided, device=None, requires_grad=False: -1,
        torch.randn_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
        torch.ravel: lambda input: -1,
        torch.real: lambda input, out=None: -1,
        torch.vdot: lambda input, other, out=None: -1,
        torch.view_as_real: lambda input: -1,
        torch.view_as_complex: lambda input: -1,
        torch.reciprocal: lambda input, out=None: -1,
        torch.relu: lambda input, inplace=False: -1,
        torch.remainder: lambda input, other, out=None: -1,
        torch.renorm: lambda input, p, dim, maxnorm, out=None: -1,
        torch.repeat_interleave: lambda input, dim=None: -1,
        torch.reshape: lambda input, shape: -1,
        torch.rnn_relu: lambda input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first: -1,
        torch.rnn_relu_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
        torch.rnn_tanh: lambda input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first: -1,
        torch.rnn_tanh_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
        torch.roll: lambda input, shifts, dims=None: -1,
        torch.rot90: lambda input, k=1, dims=(0, 1): -1,
        torch.round: lambda input, out=None: -1,
        torch.row_stack: lambda tensors, out=None: -1,  # alias for torch.vstack
        torch._rowwise_prune: (lambda weight, mask, compressed_indices_dtype: -1),
        torch.rrelu: lambda input, lower=1. / 8, upper=1. / 3, training=False, inplace=False: -1,
        torch.rsqrt: lambda input, out=None: -1,
        torch.rsub: lambda input, other, alpha=1: -1,
        torch.saddmm: lambda input, mat1, mat2, beta=1, alpha=1, out=None: -1,
        torch.scatter: lambda input, dim, index, src: -1,
        torch.scatter_add: lambda input, dim, index, src: -1,
        torch.scatter_reduce: lambda input, dim, index, src, reduce, include_self=True: -1,
        torch.searchsorted: lambda sorted_sequence, input, out_int32=False, right=False, out=None: -1,
        torch.segment_reduce: lambda data, reduce="max", lengths=None, indices=None, offsets=None, axis=0, unsafe=False: -1,
        torch.select: lambda input, dim, index: -1,
        torch.select_scatter: lambda input, src, dim, index: -1,
        torch.slice_scatter: lambda input, src, dim=0, start=None, end=None, step=1: -1,
        torch.selu: lambda input, inplace=False: -1,
        torch.sigmoid: lambda input, out=None: -1,
        torch.sign: lambda input, out=None: -1,
        torch.signbit: lambda input, out=None: -1,
        torch.sgn: lambda input, out=None: -1,
        torch.sin: lambda input, out=None: -1,
        torch.sinc: lambda input, out=None: -1,
        torch.sinh: lambda input, out=None: -1,
        torch.slogdet: lambda input: -1,
        torch.linalg.slogdet: lambda input: -1,
        torch.smm: lambda input, mat2: -1,
        torch.spmm: lambda input, mat2: -1,
        torch.softmax: lambda input, dim, dtype=None: -1,
        torch.linalg.solve: lambda A, B, left=True, out=None: -1,
        torch.linalg.solve_ex: lambda A, B, left=True, check_errors=False, out=None: -1,
        torch.sort: lambda input, dim=-1, descending=False, *, stable=False, out=None: -1,
        torch.split: lambda tensor, split_size_or_sections, dim=0: -1,
        torch.split_with_sizes: lambda tensor, split_size_or_sections, dim=0: -1,
        torch.sqrt: lambda input, out=None: -1,
        torch.square: lambda input, out=None: -1,
        torch.squeeze: lambda input, dim=None, out=None: -1,
        torch.sspaddmm: lambda input, mat1, mat2, beta=1, alpha=1, out=None: -1,
        torch.stack: lambda tensors, dim=0, out=None: -1,
        torch.std: lambda input, dim=None: -1,
        torch.std_mean: lambda input, dim=None: -1,
        torch.stft: (lambda input, n_fft, hop_length=None, win_length=None, window=None, center=True,
                     pad_mode='reflect', normalized=False, onesided=True, return_complex=None: -1),
        torch.sub: lambda input, other, out=None: -1,
        torch.subtract: lambda input, other, out=None: -1,
        torch.sum: lambda input, dim=None: -1,
        torch.nansum: lambda input, dim=None: -1,
        torch.svd: lambda input, some=True, compute_uv=True, out=None: -1,
        torch.svd_lowrank: lambda input, q=6, niter=2, M=None: -1,
        torch.linalg.svd: lambda input, full_matrices=True, out=None: -1,
        torch.linalg.svdvals: lambda input, out=None: -1,
        torch.symeig: lambda input, eigenvectors=False, upper=True, out=None: -1,
        torch.swapaxes: lambda input, dim0, dim1: -1,
        torch.swapdims: lambda input, axis0, axis1: -1,
        torch.special.airy_ai: lambda input: -1,
        torch.special.bessel_j0: lambda input: -1,
        torch.special.bessel_j1: lambda input: -1,
        torch.special.bessel_y0: lambda input: -1,
        torch.special.bessel_y1: lambda input: -1,
        torch.special.chebyshev_polynomial_t: lambda input, n, out=None: -1,
        torch.special.chebyshev_polynomial_u: lambda input, n, out=None: -1,
        torch.special.chebyshev_polynomial_v: lambda input, n, out=None: -1,
        torch.special.chebyshev_polynomial_w: lambda input, n, out=None: -1,
        torch.special.digamma: lambda input: -1,
        torch.special.entr: lambda input: -1,
        torch.special.erf: lambda input: -1,
        torch.special.erfc: lambda input: -1,
        torch.special.erfcx: lambda input: -1,
        torch.special.erfinv: lambda input: -1,
        torch.special.exp2: lambda input: -1,
        torch.special.expit: lambda input: -1,
        torch.special.expm1: lambda input: -1,
        torch.special.gammainc: lambda input, other, out=None: -1,
        torch.special.gammaincc: lambda input, other, out=None: -1,
        torch.special.gammaln: lambda input: -1,
        torch.special.hermite_polynomial_h: lambda input, n, out=None: -1,
        torch.special.hermite_polynomial_he: lambda input, n, out=None: -1,
        torch.special.i0: lambda input: -1,
        torch.special.i0e: lambda input: -1,
        torch.special.i1: lambda input: -1,
        torch.special.i1e: lambda input: -1,
        torch.special.laguerre_polynomial_l: lambda input, n, out=None: -1,
        torch.special.legendre_polynomial_p: lambda input, n, out=None: -1,
        torch.special.log1p: lambda input: -1,
        torch.special.log_ndtr: lambda input: -1,
        torch.special.log_softmax: lambda input, dim, dtype=None: -1,
        torch.special.logit: lambda input: -1,
        torch.special.logsumexp: lambda input, dim, keepdim=False, out=None: -1,
        torch.special.modified_bessel_i0: lambda input: -1,
        torch.special.modified_bessel_i1: lambda input: -1,
        torch.special.modified_bessel_k0: lambda input: -1,
        torch.special.modified_bessel_k1: lambda input: -1,
        torch.special.multigammaln: lambda input, p: -1,
        torch.special.ndtr: lambda input: -1,
        torch.special.ndtri: lambda input: -1,
        torch.special.polygamma: lambda input, n, out=None: -1,
        torch.special.psi: lambda input: -1,
        torch.special.round: lambda input: -1,
        torch.special.scaled_modified_bessel_k0: lambda input: -1,
        torch.special.scaled_modified_bessel_k1: lambda input: -1,
        torch.special.shifted_chebyshev_polynomial_t: lambda input, n, out=None: -1,
        torch.special.shifted_chebyshev_polynomial_u: lambda input, n, out=None: -1,
        torch.special.shifted_chebyshev_polynomial_v: lambda input, n, out=None: -1,
        torch.special.shifted_chebyshev_polynomial_w: lambda input, n, out=None: -1,
        torch.special.sinc: lambda input: -1,
        torch.special.softmax: lambda input, dim, dtype=None: -1,
        torch.special.spherical_bessel_j0: lambda input: -1,
        torch.special.xlog1py: lambda input, other, out=None: -1,
        torch.special.xlogy: lambda input, other, out=None: -1,
        torch.special.zeta: lambda self, other, out=None: -1,
        torch.t: lambda input: -1,
        torch.take: lambda input, index: -1,
        torch.take_along_dim: lambda input, indices, dim=None, out=None: -1,
        torch.tan: lambda input, out=None: -1,
        torch.tanh: lambda input, out=None: -1,
        torch.linalg.tensorinv: lambda a, ind=2: -1,
        torch.linalg.tensorsolve: lambda a, b, dims=None: -1,
        torch.tensordot: lambda a, b, dims=2, out=None: -1,
        torch.tensor_split: lambda input, indices_or_sections, dim=0: -1,
        torch.threshold: lambda input, threshold, value, inplace=False: -1,
        torch.tile: lambda input, dims: -1,
        torch.topk: lambda input, k, dim=-1, descending=False, out=None: -1,
        torch.trace: lambda input: -1,
        torch.transpose: lambda input, dim0, dim1: -1,
        torch.trapz: lambda y, x=None, dim=-1: -1,
        torch.trapezoid: lambda y, x=None, dim=-1: -1,
        torch.triangular_solve: lambda input, A, upper=True, transpose=False, unitriangular=False: -1,
        torch.linalg.solve_triangular: lambda input, B, upper, left=True, unitriangular=False: -1,
        torch.tril: lambda input, diagonal=0, out=None: -1,
        torch.triplet_margin_loss: (lambda anchor, positive, negative, margin=1.0, p=2, eps=1e-06, swap=False,

                                    size_average=None, reduce=None, reduction='mean': -1),
        torch.triu: lambda input, diagonal=0, out=None: -1,
        torch.true_divide: lambda input, other: -1,
        torch.trunc: lambda input, out=None: -1,
        torch.unbind: lambda input, dim=0: -1,
        torch.unique: lambda input, sorted=True, return_inverse=False, return_counts=False, dim=None: -1,
        torch.unique_consecutive: lambda input, return_inverse=False, return_counts=False, dim=None: -1,
        torch.unsafe_chunk: lambda input, chunks, dim=0: -1,
        torch.unsafe_split: lambda tensor, split_size_or_sections, dim=0: -1,
        torch.unsafe_split_with_sizes: lambda tensor, split_size_or_sections, dim=0: -1,
        torch.unsqueeze: lambda input, dim, out=None: -1,
        torch.linalg.vander: lambda x, N=None: -1,
        torch.var: lambda input, dim=None: -1,
        torch.var_mean: lambda input, dim=None: -1,
        torch.vsplit: lambda input, indices_or_sections: -1,
        torch.vstack: lambda tensors, out=None: -1,
        torch.where: lambda condition, x=None, y=None: -1,
        torch.zeros_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
        torch._fw_primal_copy: lambda self, level: -1,
        torch._make_dual_copy: lambda primal, tangent, level: -1,
        torch.view_as_real_copy: lambda self: -1,
        torch.view_as_complex_copy: lambda self: -1,
        torch._conj_copy: lambda self: -1,
        torch._neg_view_copy: lambda self: -1,
        torch.as_strided_copy: lambda self, size, stride, storage_offset=None: -1,
        torch._sparse_broadcast_to_copy: lambda self, size: -1,
        torch.diagonal_copy: lambda self, offset=0, dim1=0, dim2=1: -1,
        torch.expand_copy: lambda self, size, *, implicit=False: -1,
        torch.narrow_copy: lambda self, dim, start, length: -1,
        torch.permute_copy: lambda self, dims: -1,
        torch._reshape_alias_copy: lambda self, size, stride: -1,
        torch.select_copy: lambda self, dim, index: -1,
        torch.detach_copy: lambda self: -1,
        torch.slice_copy: lambda self, dim=0, start=None, end=None, step=1: -1,
        torch.split_copy: lambda self, split_size, dim=0: -1,
        torch.split_with_sizes_copy: lambda self, split_sizes, dim=0: -1,
        torch.squeeze_copy: lambda self: -1,
        torch.squeeze_copy: lambda self, dim: -1,
        torch.t_copy: lambda self: -1,
        torch.transpose_copy: lambda self, dim0, dim1: -1,
        torch.unsqueeze_copy: lambda self, dim: -1,
        torch._indices_copy: lambda self: -1,
        torch._values_copy: lambda self: -1,
        torch.indices_copy: lambda self: -1,
        torch.values_copy: lambda self: -1,
        torch.crow_indices_copy: lambda self: -1,
        torch.col_indices_copy: lambda self: -1,
        torch.ccol_indices_copy: lambda self: -1,
        torch.row_indices_copy: lambda self: -1,
        torch.unbind_copy: lambda self, dim=0: -1,
        torch.view_copy: lambda self, size: -1,
        torch.view_copy: lambda self, dtype: -1,
        torch.unfold_copy: lambda self, dimension, size, step: -1,
        torch.alias_copy: lambda self: -1,
        Tensor.__floordiv__: lambda self, other: -1,
        Tensor.__rfloordiv__: lambda self, other: -1,
        Tensor.__ifloordiv__: lambda self, other: -1,
        Tensor.__truediv__: lambda self, other: -1,
        Tensor.__rtruediv__: lambda self, other: -1,
        Tensor.__itruediv__: lambda self, other: -1,
        Tensor.__lshift__: lambda self, other: -1,
        Tensor.__rlshift__: lambda self, other: -1,
        Tensor.__ilshift__: lambda self, other: -1,
        Tensor.__rshift__: lambda self, other: -1,
        Tensor.__rrshift__: lambda self, other: -1,
        Tensor.__irshift__: lambda self, other: -1,
        Tensor.__and__: lambda self, other: -1,
        Tensor.__or__: lambda self, other: -1,
        Tensor.__xor__: lambda self, other: -1,
        Tensor.__float__: lambda self: -1,
        Tensor.__complex__: lambda self: -1,
        Tensor.__array__: lambda self, dtype: -1,
        Tensor.__bool__: lambda self: -1,
        Tensor.__contains__: lambda self, other: -1,
        Tensor.__neg__: lambda self: -1,
        Tensor.__invert__: lambda self: -1,
        Tensor.__mod__: lambda self, other: -1,
        Tensor.__rmod__: lambda self, other: -1,
        Tensor.__imod__: lambda self, other: -1,
        Tensor.__array_wrap__: lambda self, array: -1,
        Tensor.__getitem__: lambda self, idx: -1,
        Tensor.__deepcopy__: lambda self, memo: -1,
        Tensor.__int__: lambda self: -1,
        Tensor.__long__: lambda self: -1,
        Tensor.__hash__: lambda self: -1,
        Tensor.__index__: lambda self: -1,
        Tensor.__len__: lambda self: -1,
        Tensor.__format__: lambda self, format_spec: -1,
        Tensor.__reduce_ex__: lambda self, proto: -1,
        Tensor.__reversed__: lambda self: -1,
        Tensor.__repr__: lambda self, *, tensor_contents=None: -1,
        Tensor.__setitem__: lambda self, k, v: -1,
        Tensor.__setstate__: lambda self, d: -1,
        Tensor.T.__get__: lambda self: -1,
        Tensor.H.__get__: lambda self: -1,
        Tensor.mT.__get__: lambda self: -1,
        Tensor.mH.__get__: lambda self: -1,
        Tensor._backward_hooks.__get__: lambda self: -1,
        Tensor._base.__get__: lambda self: -1,
        Tensor._cdata.__get__: lambda self: -1,
        Tensor.grad.__get__: lambda self: -1,
        Tensor._grad.__get__: lambda self: -1,
        Tensor._grad_fn.__get__: lambda self: -1,
        Tensor.grad_fn.__get__: lambda self: -1,
        Tensor._version.__get__: lambda self: -1,
        Tensor._autocast_to_reduced_precision: lambda self, cuda_enabled, cpu_enabled, cuda_dtype, cpu_dtype: -1,
        Tensor._autocast_to_full_precision: lambda self, cuda_enabled, cpu_enabled: -1,
        Tensor.data.__get__: lambda self: -1,
        Tensor.device.__get__: lambda self: -1,
        Tensor.dtype.__get__: lambda self: -1,
        Tensor.is_cuda.__get__: lambda self: -1,
        Tensor.is_cpu.__get__: lambda self: -1,
        Tensor.is_xpu.__get__: lambda self: -1,
        Tensor.is_ipu.__get__: lambda self: -1,
        Tensor.is_leaf.__get__: lambda self: -1,
        Tensor.retains_grad.__get__: lambda self: -1,
        Tensor.is_meta.__get__: lambda self: -1,
        Tensor.is_mps.__get__: lambda self: -1,
        Tensor.is_nested.__get__: lambda self: -1,
        Tensor.is_ort.__get__: lambda self: -1,
        Tensor.is_mkldnn.__get__: lambda self: -1,
        Tensor.is_quantized.__get__: lambda self: -1,
        Tensor.is_sparse.__get__: lambda self: -1,
        Tensor.is_sparse_csr.__get__: lambda self: -1,
        Tensor.is_vulkan.__get__: lambda self: -1,
        Tensor.layout.__get__: lambda self: -1,
        Tensor.name.__get__: lambda self: -1,
        Tensor.names.__get__: lambda self: -1,
        Tensor.ndim.__get__: lambda self: -1,
        Tensor.output_nr.__get__: lambda self: -1,
        Tensor.requires_grad.__get__: lambda self: -1,
        Tensor.shape.__get__: lambda self: -1,
        Tensor.volatile.__get__: lambda self: -1,
        Tensor.real.__get__: lambda self: -1,
        Tensor.imag.__get__: lambda self: -1,
        Tensor.__cuda_array_interface__.__get__: lambda self: -1,
        Tensor.type: lambda self, dtype=None, non_blocking=False, **kwargs: -1,
        Tensor._coalesced_: lambda self: -1,
        Tensor._dimI: lambda self: -1,
        Tensor._dimV: lambda self: -1,
        Tensor._indices: lambda self: -1,
        Tensor._is_view: lambda self: -1,
        Tensor._nnz: lambda self: -1,
        Tensor.crow_indices: lambda self: -1,
        Tensor.col_indices: lambda self: -1,
        Tensor.ccol_indices: lambda self: -1,
        Tensor.row_indices: lambda self: -1,
        Tensor._update_names: lambda self, names, inplace: -1,
        Tensor._values: lambda self: -1,
        Tensor.adjoint: lambda self: -1,
        Tensor.align_as: lambda self, other: -1,
        Tensor.align_to: lambda self, order, ellipsis_idx: -1,
        Tensor.apply_: lambda self, callable: -1,
        Tensor.as_strided: lambda self, size, stride: -1,
        Tensor.as_strided_: lambda self, size, stride: -1,
        Tensor.backward: lambda self, gradient=None, retain_graph=None, create_graph=False, inputs=None: -1,
        Tensor.bfloat16: lambda self, memory_format=torch.preserve_format: -1,
        Tensor.bool: lambda self, memory_format=torch.preserve_format: -1,
        Tensor.byte: lambda self, memory_format=torch.preserve_format: -1,
        Tensor.char: lambda self, memory_format=torch.preserve_format: -1,
        Tensor.cauchy_: lambda self, median=0, sigma=1, *, generator=None: -1,
        Tensor.coalesce: lambda self: -1,
        Tensor._coalesced_: lambda self, coalesced: -1,
        Tensor.contiguous: lambda self, memory_format=torch.contiguous_format: -1,
        Tensor.copy_: lambda self, src, non_blocking=False: -1,
        Tensor.cpu: lambda self, memory_format=torch.preserve_format: -1,
        Tensor.cuda: lambda self, memory_format=torch.preserve_format: -1,
        Tensor.xpu: lambda self, memory_format=torch.preserve_format: -1,
        Tensor.ipu: lambda self, memory_format=torch.preserve_format: -1,
        Tensor.data_ptr: lambda self: -1,
        Tensor.dense_dim: lambda self: -1,
        Tensor.diagonal_scatter: lambda self, src, offset=0, dim1=0, dim2=1: -1,
        Tensor.dim: lambda self: -1,
        Tensor.double: lambda self, memory_format=torch.preserve_format: -1,
        Tensor.cdouble: lambda self, memory_format=torch.preserve_format: -1,
        Tensor.element_size: lambda self: -1,
        Tensor.expand: lambda self, size: -1,
        Tensor.expand_as: lambda self, other: -1,
        Tensor.exponential_: lambda self, lambd=1, *, generator=None: -1,
        Tensor.fill_: lambda self, value: -1,
        Tensor.fill_diagonal_: lambda self, value: -1,
        Tensor.float: lambda self, memory_format=torch.preserve_format: -1,
        Tensor.cfloat: lambda self, memory_format=torch.preserve_format: -1,
        Tensor.geometric_: lambda self, p, *, generator=None: -1,
        Tensor.get_device: lambda self: -1,
        Tensor.half: lambda self, memory_format=torch.preserve_format: -1,
        Tensor.chalf: lambda self, memory_format=torch.preserve_format: -1,
        Tensor.has_names: lambda self: -1,
        Tensor.indices: lambda self: -1,
        Tensor.int: lambda self, memory_format=torch.preserve_format: -1,
        Tensor.is_coalesced: lambda self: -1,
        Tensor.is_contiguous: lambda self: -1,
        Tensor.is_inference: lambda self: -1,
        Tensor.is_pinned: lambda self: -1,
        Tensor.is_set_to: lambda self, tensor: -1,
        Tensor.is_shared: lambda self: -1,
        Tensor.item: lambda self: -1,
        Tensor.log_normal_: lambda self, mean=1, std=2, *, generator=None: -1,
        Tensor.log_softmax: lambda self, dim: -1,
        Tensor.long: lambda self, memory_format=torch.preserve_format: -1,
        Tensor.map_: lambda self, tensor, callable: -1,
        Tensor.map2_: lambda self, x, y, callable: -1,
        Tensor.mm: lambda self, mat2: -1,
        Tensor.narrow_copy: lambda self, dimension, start, length: -1,
        Tensor.ndimension: lambda self: -1,
        Tensor.nelement: lambda self: -1,
        Tensor._nested_tensor_size: lambda self: -1,
        Tensor.normal_: lambda self: -1,
        Tensor.numpy: lambda self: -1,
        Tensor.permute: lambda self, dim: -1,
        Tensor.pin_memory: lambda self: -1,
        Tensor.put_: lambda self, indices, tensor, accumulate=False: -1,
        Tensor.qscheme: lambda self: -1,
        Tensor.random_: lambda self, from_=0, to=None, *, generator=None: -1,
        Tensor.record_stream: lambda self, stream: -1,
        Tensor.refine_names: lambda self, names: -1,
        Tensor.register_hook: lambda self, hook: -1,
        Tensor.rename: lambda self, name: -1,
        Tensor.repeat: lambda self, *size: -1,
        Tensor.requires_grad_: lambda self, requires_grad=True: -1,
        Tensor.reshape_as: lambda self, other: -1,
        Tensor.resize: lambda self, *size: -1,
        Tensor.resize_: lambda self, size: -1,
        Tensor.resize_as: lambda self, other: -1,
        Tensor.resize_as_sparse_: lambda self, other: -1,
        Tensor.retain_grad: lambda self: -1,
        Tensor.set_: lambda self, source=None, storage_offset=0, size=None, stride=None: -1,
        Tensor.select_scatter: lambda self, src, dim, index: -1,
        Tensor.share_memory_: lambda self: -1,
        Tensor.short: lambda self, memory_format=torch.preserve_format: -1,
        Tensor.size: lambda self: -1,
        Tensor.slice_scatter: lambda self, src, dim=0, start=None, end=None, step=1: -1,
        Tensor.sparse_dim: lambda self: -1,
        Tensor.sparse_mask: lambda self, mask: -1,
        Tensor.sparse_resize_: lambda self, size1, size2, dense_dim: -1,
        Tensor.sparse_resize_and_clear_: lambda self, size1, size2, dense_dim: -1,
        Tensor.sspaddmm: lambda self, mat1, mat2, beta=1, alpha=1, out=None: -1,
        Tensor.storage: lambda self: -1,
        Tensor._storage: lambda self: -1,
        Tensor.storage_offset: lambda self: -1,
        Tensor.storage_type: lambda self: -1,
        Tensor.sum_to_size: lambda self, size: -1,
        Tensor.tile: lambda self, *reps: -1,
        Tensor.to: lambda self, dtype, non_blocking=False, copy=False, memory_format=torch.preserve_format: -1,
        Tensor.to_dense: lambda self, dtype=None: -1,
        Tensor._to_dense: lambda self, dtype=None: -1,
        Tensor.to_sparse: lambda self: -1,
        Tensor.tolist: lambda self: -1,
        Tensor.to_mkldnn: lambda self: -1,
        Tensor.type_as: lambda self, other: -1,
        Tensor.unfold: lambda self, dimension, size, step: -1,
        Tensor.uniform_: lambda self, from_=0, to=1: -1,
        Tensor.values: lambda self: -1,
        Tensor.view: lambda self, shape: -1,
        Tensor.view_as: lambda self, other: -1,
        Tensor.zero_: lambda self: -1,
        Tensor.__dlpack__: lambda self, stream=None: -1,
        Tensor.__dlpack_device__: lambda self: -1,
        torch.linalg.lstsq: lambda self, b, cond=None, driver=None: -1,
    }

    ret2 = {}
    ignored = get_ignored_functions()

    for k, v in ret.items():
        # Generate methods like __add__ and add_ by default from add
        names = [
            k.__name__,  # Default method
            k.__name__ + "_",  # Inplace variant
            "__" + k.__name__ + "__",  # Dunder method
            "__i" + k.__name__ + "__",  # Inplace dunder method
            "__r" + k.__name__ + "__",  # Reverse dunder method
        ]

        if k.__name__.startswith("bitwise_"):
            # bitwise_<op> have dunder methods of the form __<op>__
            # And so on.
            subname = k.__name__[len("bitwise_"):]
            names.extend([
                "__" + subname + "__",
                "__i" + subname + "__",
                "__r" + subname + "__"
            ])

        for name in names:
            func = getattr(Tensor, name, None)
            if callable(func) and func not in ret and func not in ignored:
                ret2[func] = v

    ret.update(ret2)
    return ret

def wrap_torch_function(dispatcher: Callable):
    """Wraps a given function with ``__torch_function__`` -related functionality.

    Parameters
    ----------
    dispatcher: Callable
        A callable that returns an iterable of Tensor-likes passed into the function.

    Note
    ----
    This decorator may reduce the performance of your code. Generally, it's enough to express
    your code as a series of functions that, themselves, support __torch_function__. If you
    find yourself in the rare situation where this is not the case, e.g. if you're wrapping a
    low-level library and you also need it to work for Tensor-likes, then this function is available.

    Examples
    --------
    >>> def dispatcher(a): # Must have the same signature as func
    ...     return (a,)
    >>> @torch.overrides.wrap_torch_function(dispatcher)
    >>> def func(a): # This will make func dispatchable by __torch_function__
    ...     return a + 0
    """
    def inner(func):
        @functools.wraps(func)
        def wrapped(*args, **kwargs):
            relevant_args = dispatcher(*args, **kwargs)
            if has_torch_function(relevant_args):
                return handle_torch_function(wrapped, relevant_args, *args, **kwargs)

            return func(*args, **kwargs)

        return wrapped

    return inner

def _get_overloaded_args(relevant_args: Iterable[Any]) -> List[Any]:
    """Returns a list of arguments on which to call __torch_function__.

    Checks arguments in relevant_args for __torch_function__ implementations,
    storing references to the arguments and their types in overloaded_args and
    overloaded_types in order of calling precedence. Only distinct types are
    considered. If a type is a subclass of another type it will have higher
    precedence, otherwise the precedence order is the same as the order of
    arguments in relevant_args, that is, from left-to-right in the argument list.

    The precedence-determining algorithm implemented in this function is
    described in `NEP-0018`_.

    See torch::append_overloaded_arg for the equivalent function in the C++
    implementation.

    Parameters
    ----------
    relevant_args : iterable of array-like
        Iterable of array-like arguments to check for __torch_function__
        methods.

    Returns
    -------
    overloaded_args : list
        Arguments from relevant_args on which to call __torch_function__
        methods, in the order in which they should be called.

    .. _NEP-0018:
       https://numpy.org/neps/nep-0018-array-function-protocol.html
    """
    # If torch function is not enabled, there are no overloaded types
    if not torch._C._is_torch_function_enabled():
        return []
    # Runtime is O(num_arguments * num_unique_types)
    overloaded_types: Set[Type] = set()
    overloaded_args: List[Any] = []
    for arg in relevant_args:
        arg_type = type(arg)
        # We only collect arguments if they have a unique type, which ensures
        # reasonable performance even with a long list of possibly overloaded
        # arguments.
        #
        # NB: Important to exclude _disabled_torch_function_impl, otherwise
        # https://github.com/pytorch/pytorch/issues/64687
        if (arg_type not in overloaded_types and hasattr(arg_type, '__torch_function__') and
                arg_type.__torch_function__ != torch._C._disabled_torch_function_impl):
            # Create lists explicitly for the first type (usually the only one
            # done) to avoid setting up the iterator for overloaded_args.
            if overloaded_types:
                overloaded_types.add(arg_type)
                # By default, insert argument at the end, but if it is
                # subclass of another argument, insert it before that argument.
                # This ensures "subclasses before superclasses".
                index = len(overloaded_args)
                for i, old_arg in enumerate(overloaded_args):
                    if issubclass(arg_type, type(old_arg)):
                        index = i
                        break
                overloaded_args.insert(index, arg)
            else:
                overloaded_types = {arg_type}
                overloaded_args = [arg]
    return overloaded_args


def handle_torch_function(
        public_api: Callable, relevant_args: Iterable[Any], *args, **kwargs) -> Any:
    """Implement a function with checks for ``__torch_function__`` overrides.

    See torch::autograd::handle_torch_function for the equivalent of this
    function in the C++ implementation.

    Arguments
    ---------
    public_api : function
        Function exposed by the public torch API originally called like
        ``public_api(*args, **kwargs)`` on which arguments are now being
        checked.
    relevant_args : iterable
        Iterable of arguments to check for __torch_function__ methods.
    args : tuple
        Arbitrary positional arguments originally passed into ``public_api``.
    kwargs : tuple
        Arbitrary keyword arguments originally passed into ``public_api``.

    Returns
    -------
    object
        Result from calling ``implementation`` or an ``__torch_function__``
        method, as appropriate.

    Raises
    ------
    TypeError : if no implementation is found.

    Example
    -------
    >>> def func(a):
    ...     if has_torch_function_unary(a):
    ...         return handle_torch_function(func, (a,), a)
    ...     return a + 0
    """
    # Check for __torch_function__ methods.
    overloaded_args = _get_overloaded_args(relevant_args)
    # overloaded_args already have unique types.
    types = tuple(map(type, overloaded_args))

    # Check for __torch_function__ mode.
    mode = _get_torch_function_mode()
    if mode is not None:
        # NB: unlike on tensors, modes are instances
        with _no_torch_function_mode():
            result = mode.__torch_function__(public_api, types, args, kwargs)
        if result is not NotImplemented:
            return result

    # Call overrides
    for overloaded_arg in overloaded_args:
        # This call needs to become a classmethod call in the future.
        # See https://github.com/pytorch/pytorch/issues/63767
        torch_func_method = overloaded_arg.__torch_function__
        if hasattr(torch_func_method, "__self__") and torch_func_method.__self__ is overloaded_arg and \
                torch_func_method is not torch._C._disabled_torch_function_impl:
            warnings.warn("Defining your `__torch_function__ as a plain method is deprecated and "
                          "will be an error in future, please define it as a classmethod.",
                          DeprecationWarning)

        # Use `public_api` instead of `implementation` so __torch_function__
        # implementations can do equality/identity comparisons.
        result = torch_func_method(public_api, types, args, kwargs)

        if result is not NotImplemented:
            return result

    func_name = '{}.{}'.format(public_api.__module__, public_api.__name__)
    msg = (
        "no implementation found for '{}' on types that implement "
        '__torch_function__: {}'
    ).format(func_name, [type(arg) for arg in overloaded_args])
    if mode is not None:
        msg += f" nor in mode {mode}"
    raise TypeError(msg)

has_torch_function = _add_docstr(
    _has_torch_function,
    r"""Check for __torch_function__ implementations in the elements of an iterable
    or if a __torch_function__ mode is enabled.  Considers exact ``Tensor`` s
    and ``Parameter`` s non-dispatchable.  Use this to guard a call to
    :func:`handle_torch_function`; don't use it to test if something
    is Tensor-like, use :func:`is_tensor_like` instead.
    Arguments
    ---------
    relevant_args : iterable
        Iterable or aguments to check for __torch_function__ methods.
    Returns
    -------
    bool
        True if any of the elements of relevant_args have __torch_function__
        implementations, False otherwise.
    See Also
    ________
    torch.is_tensor_like
        Checks if something is a Tensor-like, including an exact ``Tensor``.
    """
)

has_torch_function_unary = _add_docstr(
    _has_torch_function_unary,
    r"""Special case of `has_torch_function` for single inputs.
    Instead of:
      `has_torch_function((t,))`
    call:
      `has_torch_function_unary(t)`
    which skips unnecessary packing and unpacking work.
    """
)

has_torch_function_variadic = _add_docstr(
    _has_torch_function_variadic,
    r"""Special case of `has_torch_function` that skips tuple creation.

    This uses the METH_FASTCALL protocol introduced in Python 3.7

    Instead of:
      `has_torch_function((a, b))`
    call:
      `has_torch_function_variadic(a, b)`
    which skips unnecessary packing and unpacking work.
    """
)

@functools.lru_cache(None)
def _get_overridable_functions() -> Tuple[Dict[Any, List[Callable]], Dict[Callable, str]]:
    overridable_funcs = collections.defaultdict(list)
    index = {}
    tested_namespaces = [
        ("torch", torch, torch.__all__ + dir(torch._C._VariableFunctions)),
        ("torch.functional", torch.functional, torch.functional.__all__),
        ("torch.nn.functional", torch.nn.functional, dir(torch.nn.functional)),
        ("torch.nn.init", torch.nn.init, dir(torch.nn.init)),
        ("torch.Tensor", torch.Tensor, dir(torch.Tensor)),
        ("torch.linalg", torch.linalg, dir(torch.linalg)),
        ("torch.fft", torch.fft, dir(torch.fft)),
        ("torch.special", torch.special, dir(torch.special)),
    ]
    for namespace_str, namespace, ns_funcs in tested_namespaces:
        for func_name in ns_funcs:
            ignore = False
            # ignore private functions or functions that are deleted in torch.__init__
            if namespace is not torch.Tensor:
                if func_name.startswith('__'):
                    continue
                elif func_name.startswith('_'):
                    ignore = True
                elif func_name.endswith('_'):
                    ignore = True
                elif not func_name[0].islower():
                    ignore = True
                elif func_name == 'unique_dim':
                    continue
            else:
                func = getattr(namespace, func_name)
                if getattr(object, func_name, None) == func:
                    continue
                if func_name == '__weakref__':
                    continue
            func = getattr(namespace, func_name)
            if namespace is torch.Tensor and getattr(object, func_name, None) == func:
                continue
            # ignore re-exported modules
            if isinstance(func, types.ModuleType):
                continue
            # ignore __future__ imports
            if isinstance(func, __future__._Feature):
                continue

            if not callable(func) and hasattr(func, "__get__"):
                index[func.__get__] = f"{namespace_str}.{func_name}.__get__"
                index[func.__set__] = f"{namespace_str}.{func_name}.__set__"
                if ignore:
                    continue
                if func.__get__ in get_ignored_functions():
                    msg = ("{}.{} is in the tuple returned by torch._overrides.get_ignored_functions "
                           "but still has an explicit override")
                    assert func.__get__ not in get_testing_overrides(), msg.format(namespace, func.__name__)
                    continue
                else:
                    overridable_funcs[func].append(func.__get__)
                    continue

            if not callable(func):
                continue

            index[func] = f"{namespace_str}.{func_name}"

            if ignore:
                continue

            # cannot be overriden by __torch_function__
            if func in get_ignored_functions():
                msg = ("{}.{} is in the tuple returned by torch._overrides.get_ignored_functions "
                       "but still has an explicit override")
                assert func not in get_testing_overrides(), msg.format(namespace, func.__name__)
                continue
            overridable_funcs[namespace].append(func)
    return overridable_funcs, index

def get_overridable_functions() -> Dict[Any, List[Callable]]:
    """List functions that are overridable via __torch_function__

    Returns
    -------
    Dict[Any, List[Callable]]
        A dictionary that maps namespaces that contain overridable functions
        to functions in that namespace that can be overridden.
    """
    return _get_overridable_functions()[0]

def resolve_name(f):
    """Get a human readable string name for a function passed to
    __torch_function__

    Arguments
    ---------
    callable : Callable
        Function to resolve the name of.

    Returns
    -------
    str
        Name of the function; if eval'ed it should give back the input
        function.
    """
    if isinstance(f, torch._ops.OpOverload):
        return str(f)
    return _get_overridable_functions()[1].get(f)

@functools.lru_cache(None)
def _get_tensor_methods() -> Set[Callable]:
    """ Returns a set of the overridable methods on ``torch.Tensor`` """
    overridable_funcs = get_overridable_functions()
    methods = set(overridable_funcs[torch.Tensor])
    return methods

def is_tensor_method_or_property(func: Callable) -> bool:
    """
    Returns True if the function passed in is a handler for a
    method or property belonging to ``torch.Tensor``, as passed
    into ``__torch_function__``.

    .. note::
       For properties, their ``__get__`` method must be passed in.

    This may be needed, in particular, for the following reasons:

    1. Methods/properties sometimes don't contain a `__module__` slot.
    2. They require that the first passed-in argument is an instance
       of ``torch.Tensor``.

    Examples
    --------
    >>> is_tensor_method_or_property(torch.Tensor.add)
    True
    >>> is_tensor_method_or_property(torch.add)
    False
    """
    return func in _get_tensor_methods() or func.__name__ == "__get__"

def is_tensor_like(inp):
    """
    Returns ``True`` if the passed-in input is a Tensor-like.

    Currently, this occurs whenever there's a ``__torch_function__``
    attribute on the type of the input.

    Examples
    --------
    A subclass of tensor is generally a Tensor-like.

    >>> class SubTensor(torch.Tensor): ...
    >>> is_tensor_like(SubTensor([0]))
    True

    Built-in or user types aren't usually Tensor-like.

    >>> is_tensor_like(6)
    False
    >>> is_tensor_like(None)
    False
    >>> class NotATensor: ...
    >>> is_tensor_like(NotATensor())
    False

    But, they can be made Tensor-like by implementing __torch_function__.

    >>> class TensorLike:
    ...     @classmethod
    ...     def __torch_function__(cls, func, types, args, kwargs):
    ...         return -1
    >>> is_tensor_like(TensorLike())
    True
    """
    return type(inp) is torch.Tensor or hasattr(type(inp), "__torch_function__")


def _wrap_torch_function(f):
    @functools.wraps(f)
    def wrapped(self, *args, **kwargs):
        if isinstance(f, classmethod):
            raise RuntimeError("TorchFunctionMode's torch_function function " +
                               "should be a normal method not a class method")
        inner = getattr(self, "inner", None)

        with enable_torch_function_mode(inner):
            return f(self, *args, **kwargs)
    return wrapped


# Implementation note: I had a choice about how much of mode stacks
# to implement in Python versus in C++.  At time of writing, I did not care
# too much about implementation efficiency; however, I do care about making it
# hard for users to implement modes in the wrong way.  In the end, it turned
# out to be possible to implement mode stacks entirely from userland, with the
# C++ API providing only _get_torch_function_mode() and
# _set_torch_function_mode(), so I opted to provide some unsafe C++ bindings and
# have the bulk of the logic for managing the stack in Python, which helped
# simplify the C++ API surface.  It would also have been valid to build in the
# notion of mode stack directly into C++ but in this design it's substantially
# more difficult to interact with TorchFunctionModeMeta.
class TorchFunctionModeMeta(type):
    """
    Metaclass for :class:`TorchFunctionMode`; it does two things:

        * Adds an implicit ``inner`` kwarg to ``__init__``, to
          allow the modes to be chained together to form a stack.

        * Reenables the inner mode, so that by default PyTorch API calls
          will compositionally proceed to the next mode on the stack.

    The default behavior for the second bullet is important, as it is easy to
    accidentally write ``__torch_function__`` implementations that are not
    compositional, and the wrapping here makes the obvious code do the
    right thing (aka, this is why there is a metaclass).
    """
    def __new__(metacls, name, bases, dct):
        if '__init__' in dct:
            dct['__init__'] = _wrap_init(dct['__init__'])
        if '__torch_function__' in dct:
            dct['__torch_function__'] = _wrap_torch_function(dct['__torch_function__'])
        return super().__new__(metacls, name, bases, dct)


class TorchFunctionMode(metaclass=TorchFunctionModeMeta):
    """
    A ``TorchFunctionMode`` allows you to override the meaning of all
    ``__torch_function__`` overrideable functions within a dynamic scope,
    without having to actually create a tensor subclass or manually
    monkey-patch functions in the PyTorch API.  Some common situations
    where you should use a mode:

        * You want to override the meaning of factory functions, or other
          functions that do not otherwise take a tensor as an argument
          (these cannot be overridden with tensor subclasses).

        * You want to override the behavior of all functions without needing
          to wrap your inputs in tensor subclasses; e.g., if you are just
          interested in logging intermediate computations.

        * You want to control the order of execution of various tensor
          subclasses explicitly, rather than implicitly via the return of
          ``NotImplemented``.

    Independent subclasses of :class:`TorchFunctionMode` are compositional:
    modes can be pushed onto a stack with :func:`push_torch_function_mode`.
    When you call functions in the PyTorch API inside your
    ``__torch_function__`` implementation, by default, they will forward on to
    the next mode on the mode stack.  If you want recursively call back into
    your current ``__torch_function__`` implementation, either explicitly
    invoke ``self.__torch_function__(...)``, or use the context manager
    ``enable_torch_function_mode(self, replace=self.inner)`` to make PyTorch
    API self-referential (beware of infinite loops, in this case!)
    """
    inner: "TorchFunctionMode"

    # Force metaclass to generate constructor at the base of the hierarchy
    def __init__(self):
        pass

    def __torch_function__(self, func, types, args=(), kwargs=None):
        raise NotImplementedError()

    def __enter__(self):
        old = _get_torch_function_mode()
        if hasattr(self, "inner"):
            raise RuntimeError(f"{self} has already been used as a mode. Please use a fresh version or use restore")
        else:
            self.inner = old
            if old is None:
                self.ancestors = set()
            else:
                self.ancestors = self.inner.ancestors.union({self.inner})
        _set_torch_function_mode(self)

    def __exit__(self, exc_type, exc_val, exc_tb):
        _set_torch_function_mode(self.inner)

    @contextlib.contextmanager
    def restore(self):
        return _restore_mode(self, mode_info=_TorchFunctionModeInfo())

    @classmethod
    def push(cls, *args, **kwargs):
        return push_torch_function_mode(functools.partial(cls, *args, **kwargs))


class BaseTorchFunctionMode(TorchFunctionMode):
    def __torch_function__(self, func, types, args=(), kwargs=None):
        if kwargs is None:
            kwargs = {}
        return func(*args, **kwargs)


# This is private API as I'm not sure it's possible for users to use this
# compositionally (easy to discard too many modes).  It is useful for
# library code though, e.g., in handle_torch_function
@contextlib.contextmanager
def _no_torch_function_mode() -> Iterator[None]:
    old = _get_torch_function_mode()
    _set_torch_function_mode(None)
    try:
        yield
    finally:
        _set_torch_function_mode(old)


class _TorchFunctionModeInfo(_ModeInfo):
    def __init__(self):
        super().__init__(mode_name="torch_function", mode_class=TorchFunctionMode,
                         base_mode_class=BaseTorchFunctionMode)

    def get_mode(self):
        return _get_torch_function_mode()

    def set_mode(self, mode):
        return _set_torch_function_mode(mode)


@contextlib.contextmanager
def enable_torch_function_mode(mode, *, replace=None, ignore_preexisting=False) -> Iterator[None]:
    """
    Context manager that sets the current :class:`TorchFunctionMode`; see the
    class for more information on what modes are.  This function is
    non-compositional; if there is already an existing mode, it will raise an
    error; prefer using :func:`push_torch_function_mode` if your
    ``__torch_function__`` implementation can defer to an inner mode.

    This function is safe to use inside a ``__torch_function__`` mode handler,
    as the mode is guaranteed to be disabled in this context.  You can use
    this context manager to reinstate the mode so that calls to overridable
    APIs recursively call back into your mode handler (this can easily cause
    infinite loops, so use with care!)

    Args:
        mode (:class:`TorchFunctionMode`, Tensor-like class or None): the
            mode to set as current mode.  If you pass a Tensor-like class,
            it will be treated as a non-compositional mode with no state,
            which is convenient if you have an existing tensor subclass
            that you'd like to apply globally in a quick and dirty way.
            Passing None will disable the current mode.
        replace (:class:`TorchFunctionMode` or Tensor-like class): the
            mode to replace.  You can use this argument to change the mode in
            a situation where you know what the current mode is (and you are
            intentionally overwriting it.)  If you don't know what the current
            mode is, use ``ignore_preexisting`` instead.
        ignore_preexisting (bool): if True, ignore any preexisting mode
            and overwrite it with the passed mode.
    """
    return _enable_mode(mode, _TorchFunctionModeInfo(), replace=replace, ignore_preexisting=ignore_preexisting)

@contextlib.contextmanager
def push_torch_function_mode(ctor) -> Iterator[TorchFunctionMode]:
    """
    Context manager that pushes a :class:`TorchFunctionMode` onto the current
    mode stack; see the class for more information on what modes are.  Stacked
    modes can delegate to each other by invoking the ``__torch_function__``
    method for the ``inner`` mode.

    Args:
        ctor: a function that when invoked as ``ctor(inner=...)`` produces
            a :class:`TorchFunctionMode`.  If your :class:`TorchFunctionMode`
            has no ``__init__`` implementation, you can simply pass the class
            itself (e.g., ``push_torch_function_mode(MyMode)``); otherwise,
            use ``functools.partial`` to partially apply the constructor with all
            non-inner arguments (e.g.,
            ``push_torch_function_mode(partial(MyMode, arg))``)
    """
    return _push_mode(ctor, _TorchFunctionModeInfo())

class enable_reentrant_dispatch():
    def __enter__(self):
        self._raii_guard = torch._C._RestorePythonTLSSnapshot()

    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
        del self._raii_guard

def get_buffer(tensor_subclass, data, prefix):
    import ctypes
    assert prefix in {"stride", "size", "sym_size"}
    buffer_name = f"_{prefix}_buffer"
    if not hasattr(tensor_subclass, buffer_name):
        SizeType = ctypes.c_longlong * len(data)
        setattr(tensor_subclass, buffer_name, SizeType(*data))
    ptr = ctypes.addressof(getattr(tensor_subclass, buffer_name))
    return (ptr, len(data))