code: def pad(
      input: torch.Tensor,
      pad_width: Union[Iterable[Tuple[int]], int],
      /,
      *,
      mode: Union[
          Literal[
              "constant",
              "dilated",
              "edge",
              "linear_ramp",
              "maximum",
              "mean",
              "median",
              "minimum",
              "reflect",
              "symmetric",
              "wrap",
              "empty",
          ],
          Callable,
      ] = "constant",
      stat_length: Union[Iterable[Tuple[int]], int] = 1,
      constant_values: Union[Iterable[Tuple[Number]], Number] = 0,
      end_values: Union[Iterable[Tuple[Number]], Number] = 0,
      reflect_type: Literal["even", "odd"] = "even",
      **kwargs: Optional[Any],
  ) -> torch.Tensor:
      constant_values = (
          float(constant_values)
          if not isinstance(constant_values, float)
          else constant_values
      )
      pad_width = _to_paddle_padding(pad_width, input.ndim)
      mode = (
          "replicate"
          if mode == "edge"
          else "circular"
          if mode == "wrap"
          else mode
      )
      if mode == "circular":
          return (
              torch.nn.functional.pad(
                  input.unsqueeze(0).unsqueeze(0),
                  tuple(pad_width),
                  mode=mode,
              )
              .squeeze(0)
              .squeeze(0)
          )
      elif mode == "constant":
          return torch.nn.functional.pad(
              input.unsqueeze(0),
              tuple(pad_width),
              mode=mode,
              value=constant_values,
          ).squeeze(0)
      else:
          return torch.nn.functional.pad(
              input.unsqueeze(0),
              tuple(pad_width),
              mode=mode,
          ).squeeze(0)
  
imports: from typing import Iterable, Optional, Union, Sequence, Tuple, NamedTuple, List, Literal, Callable, Any
  from numbers import Number
  from collections import namedtuple
  import torch
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from ..None import backend_version
  import ivy
  from ivy.functional.ivy.experimental.manipulation import _to_tf_padding, _check_paddle_pad, _to_paddle_padding
label_desc: Use image node_id 3 for calling a global function with example usage: pad() and returns: torch, torch, torch