code: def diff(
      x: Union[torch.Tensor, list, tuple],
      /,
      *,
      n: int = 1,
      axis: int = -1,
      prepend: Optional[
          Union[torch.Tensor, int, float, list, tuple]
      ] = None,
      append: Optional[
          Union[torch.Tensor, int, float, list, tuple]
      ] = None,
      out: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      x = x if isinstance(x, torch.Tensor) else torch.tensor(x)
      prepend = (
          prepend
          if isinstance(prepend, torch.Tensor) or prepend is None
          else torch.tensor(prepend)
      )
      append = (
          append
          if isinstance(append, torch.Tensor) or append is None
          else torch.tensor(append)
      )
      return torch.diff(
          x, n=n, dim=axis, prepend=prepend, append=append
      )
  
imports: from typing import Optional, Union, Tuple, List, Sequence
  from numbers import Number
  import torch
  import ivy
  from ivy import promote_types_of_inputs
  from ivy.functional.backends.torch.elementwise import _cast_for_unary_op
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from ..None import backend_version
label_desc: Use image node_id 11 for calling a global function with example usage: diff() and returns: torch