text: """
  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 torch
  import types
  
  def get_ignored_functions():
      """Return public functions that cannot be overrided by __torch_function__
  
      Returns
      -------
      A tuple of functions that are publicly available in the torch API but cannot
      be overrided with __torch_function__. Mostly this is because none of the
      arguments of these functions are tensors or tensor-likes.
  
      """
      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.cpp,
          torch.device,
          torch.dtype,
          torch.finfo,
          torch.has_mkl,
          torch.has_mkldnn,
          torch.has_openmp,
          torch.iinfo,
          torch.memory_format,
          torch.qscheme,
          torch.set_grad_enabled,
          torch.no_grad,
          torch.enable_grad,
          torch.layout,
          torch.align_tensors,
          torch.arange,
          torch.as_strided,
          torch.bartlett_window,
          torch.blackman_window,
          torch.can_cast,
          torch.cudnn_affine_grid_generator,
          torch.cudnn_batch_norm,
          torch.cudnn_convolution,
          torch.cudnn_convolution_transpose,
          torch.cudnn_grid_sampler,
          torch.cudnn_is_acceptable,
          torch.empty,
          torch.empty_meta,
          torch.empty_strided,
          torch.empty_quantized,
          torch.eye,
          torch.from_file,
          torch.full,
          torch.hamming_window,
          torch.hann_window,
          torch.linspace,
          torch.logspace,
          torch.mkldnn_adaptive_avg_pool2d,
          torch.mkldnn_convolution,
          torch.mkldnn_convolution_backward_weights,
          torch.mkldnn_max_pool2d,
          torch.ones,
          torch.promote_types,
          torch.rand,
          torch.randn,
          torch.randint,
          torch.randperm,
          torch.range,
          torch.sparse_coo_tensor,
          torch.vander,
          torch.zeros,
          torch.nn.functional.assert_int_or_pair,
          torch.nn.functional.boolean_dispatch,
          torch.nn.functional.division,
          torch.nn.functional.upsample,
          torch.nn.functional.upsample_bilinear,
          torch.nn.functional.upsample_nearest,
          torch.nn.functional.has_torch_function,
          torch.nn.functional.handle_torch_function,
          torch.nn.functional.sigmoid,
          torch.nn.functional.hardsigmoid,
          torch.nn.functional.tanh,
          torch.set_autocast_enabled,
          torch.is_autocast_enabled,
          torch.clear_autocast_cache,
          torch.autocast_increment_nesting,
          torch.autocast_decrement_nesting,
          torch.nn.functional.hardswish,
          torch.is_vulkan_available,
          torch._is_deterministic,
          torch._set_deterministic
      )
  
  def get_testing_overrides():
      """Return a dict containing dummy overrides for all overridable functions
  
      Returns
      -------
      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__.
  
      """
      # Every function in the PyTorch API 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.
      return {
          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.acosh: 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: -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.angle: lambda input, out=None: -1,
          torch.any: lambda input, dim, keepdim=False, out=None: -1,
          torch.argmax: lambda input: -1,
          torch.argmin: lambda input: -1,
          torch.argsort: lambda input: -1,
          torch.asin: lambda input, out=None: -1,
          torch.asinh: lambda input, out=None: -1,
          torch.atan: lambda input, out=None: -1,
          torch.atan2: lambda input, other, out=None: -1,
          torch.atanh: lambda input, out=None: -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, mean_dy, mean_dy_xmu: -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.block_diag: lambda *tensors: -1,
          torch.bmm: lambda input, mat2, out=None: -1,
          torch.broadcast_tensors: lambda *tensors: -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.cdist: lambda x1, c2, p=2, compute_mode=None: -1,
          torch.ceil: lambda input, out=None: -1,
          torch.celu: lambda input, alhpa=1., inplace=False: -1,
          torch.chain_matmul: lambda *matrices: -1,
          torch.channel_shuffle: lambda input, groups : -1,
          torch.cholesky: lambda input, upper=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.chunk: lambda input, chunks, dim=0: -1,
          torch.clamp: lambda input, min, max, out=None: -1,
          torch.clamp_min: lambda input, min, out=None: -1,
          torch.clamp_max: lambda input, max, out=None: -1,
          torch.clone: lambda input: -1,
          torch.combinations: lambda input, r=2, with_replacement=False: -1,
          torch.conj: 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.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.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.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.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.diagonal: lambda input, offset=0, dim1=0, dim2=1: -1,
          torch.digamma: lambda input, out=None: -1,
          torch.dist: lambda input, other, p=2: -1,
          torch.div: lambda input, other, out=None: -1,
          torch.dot: lambda mat1, mat2: -1,
          torch.dropout: lambda input, p, train, inplace=False: -1,
          torch.dsmm: lambda input, mat2: -1,
          torch.hsmm: lambda mat1, mat2: -1,
          torch.eig: lambda input, eigenvectors=False, 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: -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.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.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, K, N: -1,
          torch.feature_alpha_dropout: lambda input, p, train: -1,
          torch.feature_dropout: lambda input, p, train: -1,
          torch.fft: lambda input, signal_ndim, normalized=False: -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.fmod: lambda input, other, out=None: -1,
          torch.frac: 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.functional.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.ge: lambda input, other, out=None: -1,
          torch.geqrf: lambda input, out=None: -1,
          torch.ger: lambda input, vec2, out=None: -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.hardshrink: lambda input, lambd=0.5: -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.hspmm: lambda mat1, mat2, out=None: -1,
          torch.ifft: lambda input, signal_ndim, normalized=False: -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.isfinite: lambda tensor: -1,
          torch.isinf: lambda tensor: -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.irfft: lambda input, signal_ndim, normalized=False, onesided=True, signal_sizes=None: -1,
          torch.is_complex: lambda input: -1,
          torch.is_distributed: 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=True, length=None: -1),
          torch.kl_div: lambda input, target, size_average=None, reduce=None, reduction='mean', log_target=False: -1,
          torch.kthvalue: lambda input, k, dim=None, keepdim=False, out=None: -1,
          torch.layer_norm: lambda input, normalized_shape, weight=None, bias=None, esp=1e-05, cudnn_enabled=True: -1,
          torch.le: 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: -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.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, 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.lu: lambda A, pivot=True, get_infos=False, out=None: -1,
          torch.lu_solve: lambda input, 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,
          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.matrix_power: lambda input, n: -1,
          torch.matrix_rank: lambda input, tol=None, symmetric=False: -1,
          torch.max: lambda input, out=None: -1,
          torch.max_pool1d: lambda input, kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False: -1,
          torch.max_pool2d: lambda input, kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False: -1,
          torch.max_pool3d: lambda input, kernel_size, stride=None, padding=0, dilation=1, return_indices=False, 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: -1,
          torch.median: lambda input: -1,
          torch.meshgrid: lambda *tensors, **kwargs: -1,
          torch.min: lambda input, 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: -1,
          torch.mul: 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.native_batch_norm: lambda input, weight, bias, running_mean, running_var, training, momentum, eps: -1,
          torch.native_layer_norm: lambda input, weight, bias, M, N, eps: -1,
          torch.native_group_norm: lambda input, weight, bias, N, C, HxW, group, eps: -1,
          torch.native_norm: lambda input, p=2: -1,
          torch.ne: lambda input, other, out=None: -1,
          torch.neg: lambda input, 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": -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.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: -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.gelu: lambda input: -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: -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,
                                           return_indices=False, ceil_mode=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,
                                           return_indices=False, ceil_mode=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: -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.smooth_l1_loss: lambda input, target, size_average=None, reduce=None, reduction='mean': -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.unfold: lambda input, kernel_size, dilation=1, padding=0, stride=1: -1,
          torch.nonzero: lambda input, as_tuple=False: -1,
          torch.norm: lambda input, p='fro', dim=None, keepdim=False, out=None, dtype=None: -1,
          torch.norm_except_dim: lambda v, pow=2, dim=0: -1,
          torch.normal: lambda mean, std, out=None: -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 input1, input2: -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.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.pixel_shuffle: lambda input, upscale_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.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: -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.quantize_per_channel: lambda input, scales, zero_points, axis, dtype: -1,
          torch.quantize_per_tensor: lambda input, scale, zero_point, dtype: -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_pool2d: lambda input, kernel_size, stride, padding, dilation, 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, low, 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.real: lambda input, 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, repeats, dim=None: -1,
          torch.reshape: lambda input, shape: -1,
          torch.result_type: lambda tensor1, tensor2: -1,
          torch.rfft: lambda input, signal_ndim, normalized=False, onesided=True: -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, dims: -1,
          torch.round: lambda input, out=None: -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.scalar_tensor: lambda s, dtype=None, layour=None, device=None, pin_memory=None: -1,
          torch.scatter: lambda input, dim, index, src: -1,
          torch.scatter_add: lambda input, dim, index, src: -1,
          torch.searchsorted: lambda sorted_sequence, input, out_int32=False, right=False, out=None: -1,
          torch.select: lambda input, dim, index: -1,
          torch.selu: lambda input, inplace=False: -1,
          torch.sigmoid: lambda input, out=None: -1,
          torch.sign: lambda input, out=None: -1,
          torch.sin: lambda input, out=None: -1,
          torch.sinh: lambda input, out=None: -1,
          torch.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.solve: lambda input, A, out=None: -1,
          torch.sort: lambda input, dim=-1, descending=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: -1,
          torch.std_mean: lambda input: -1,
          torch.stft: (lambda input, n_fft, hop_length=None, win_length=None, window=None, center=True,
                       pad_mode='reflect', normalized=False, onesided=True: -1),
          torch.sub: lambda input, other, out=None: -1,
          torch.sum: lambda input: -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.symeig: lambda input, eigenvectors=False, upper=True, out=None: -1,
          torch.t: lambda input: -1,
          torch.take: lambda input, index: -1,
          torch.tan: lambda input, out=None: -1,
          torch.tanh: lambda input, out=None: -1,
          torch.tensordot: lambda a, b, dims=2: -1,
          torch.threshold: lambda input, threshold, value, inplace=False: -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, dim=-1: -1,
          torch.triangular_solve: lambda input, A, upper=True, transpose=False, unitriangular=False: -1,
          torch.tril: lambda input, diagonal=0, out=None: -1,
          torch.tril_indices: lambda row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided: -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.triu_indices: lambda row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided: -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.unsqueeze: lambda input, dim, out=None: -1,
          torch.var: lambda input: -1,
          torch.var_mean: lambda input: -1,
          torch.where: lambda condition, x, y: -1,
          torch.zeros_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
      }
  
  def _get_overloaded_args(relevant_args):
      """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_types : collection of types
          Types of arguments from relevant_args with __torch_function__ methods.
      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
  
      """
      # Runtime is O(num_arguments * num_unique_types)
      overloaded_types = []
      overloaded_args = []
      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.
          if (arg_type not in overloaded_types and hasattr(arg_type, '__torch_function__')):
              # 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.append(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, relevant_args, *args, **kwargs):
      """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
      -------
      Result from calling `implementation()` or an `__torch_function__`
      method, as appropriate.
  
      Raises
      ------
      TypeError : if no implementation is found.
  
      """
      # Check for __torch_function__ methods.
      overloaded_args = _get_overloaded_args(relevant_args)
      # overloaded_args already have unique types.
      types = tuple(map(type, overloaded_args))
  
      # Call overrides
      for overloaded_arg in overloaded_args:
          # Use `public_api` instead of `implementation` so __torch_function__
          # implementations can do equality/identity comparisons.
          result = overloaded_arg.__torch_function__(public_api, types, args, kwargs)
  
          if result is not NotImplemented:
              return result
  
      func_name = '{}.{}'.format(public_api.__module__, public_api.__name__)
      raise TypeError("no implementation found for '{}' on types that implement "
                      '__torch_function__: {}'
                      .format(func_name, list(map(type, overloaded_args))))
  
  def has_torch_function(relevant_args):
      """Check for __torch_function__ implementations in the elements of an iterable
  
      Arguments
      ---------
      relevant_args : iterable
          Iterable or aguments to check for __torch_function__ methods.
  
      Returns
      -------
      True if any of the elements of relevant_args have __torch_function__
      implementations, False otherwise.
      """
      return any(hasattr(a, '__torch_function__') for a in relevant_args)
  
  def get_overridable_functions():
      """List functions that are overridable via __torch_function__
  
      Returns
      -------
      A dictionary that maps namespaces that contain overridable functions
      to functions in that namespace that can be overrided.
  
      """
      overridable_funcs = collections.defaultdict(list)
      tested_namespaces = [
          (torch, torch.__all__ + dir(torch._C._VariableFunctions)),
          (torch.functional, torch.functional.__all__),
          (torch.nn.functional, dir(torch.nn.functional)),
      ]
      for namespace, ns_funcs in tested_namespaces:
          for func_name in ns_funcs:
              # ignore private functions or functions that are deleted in torch.__init__
              if func_name.startswith('_') or func_name == 'unique_dim':
                  continue
              # ignore in-place operators
              if func_name.endswith('_'):
                  continue
              # only consider objects with lowercase names
              if not func_name.islower():
                  continue
              func = getattr(namespace, func_name)
              # ignore re-exported modules
              if isinstance(func, types.ModuleType):
                  continue
              # ignore __future__ imports
              if isinstance(func, __future__._Feature):
                  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
  