code: def mean(
      x: NdarrayTensor, dim: int | tuple | None = None, **kwargs
  ) -> NdarrayTensor:
      """`torch.mean` with equivalent implementation for numpy
  
      Args:
          x: array/tensor.
  
      Returns:
          the mean of x
      """
  
      ret: NdarrayTensor
      if dim is None:
          ret = np.mean(x, **kwargs) if isinstance(x, (np.ndarray, list)) else torch.mean(x, **kwargs)  # type: ignore
      else:
          if isinstance(x, (np.ndarray, list)):
              ret = np.mean(x, axis=dim, **kwargs)
          else:
              ret = torch.mean(x, int(dim), **kwargs)  # type: ignore
  
      return ret
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  from typing import TypeVar
  import numpy
  import torch
  from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor
  from monai.utils.misc import is_module_ver_at_least
  from monai.utils.type_conversion import convert_data_type, convert_to_dst_type
label_desc: Use image node_id 28 for calling a global function with example usage: mean(x, dim) and returns: ret