code: def constant_pad(
      x: torch.Tensor,
      /,
      pad_width: List[List[int]],
      *,
      value: Number = 0.0,
      out: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      if 0 in x.shape:
          new_shape = [
              s + sum(pad_width[i]) for i, s in enumerate(x.shape)
          ]
          return torch.ones(new_shape, dtype=x.dtype) * value
      if x.shape == ():
          x = x.unsqueeze(0)
      if isinstance(pad_width, torch.Tensor):
          pad_width = pad_width.detach().cpu().numpy().tolist()
      pad_width_flat: List[int] = []
      for pad_width_sec in reversed(pad_width):
          for item in pad_width_sec:
              pad_width_flat.append(item)
      return torch.nn.functional.pad(
          x, pad_width_flat, mode="constant", value=value
      )
  
imports: import math
  from numbers import Number
  from typing import Union, Optional, Tuple, List, Sequence, Iterable
  import torch
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes
  from ivy.functional.ivy.manipulation import _calculate_out_shape
  from .None import backend_version
label_desc: Use image node_id 13 for calling a global function with example usage: constant_pad(pad_width) and returns: torch, unknown