code: def get_equivalent_dtype(dtype, data_type):
      """Convert to the `dtype` that corresponds to `data_type`.
  
      The input dtype can also be a string. e.g., `"float32"` becomes `torch.float32` or
      `np.float32` as necessary.
  
      Example::
  
          im = torch.tensor(1)
          dtype = get_equivalent_dtype(np.float32, type(im))
  
      """
      if dtype is None:
          return None
      if (
          data_type is torch.Tensor
          or data_type.__name__ == "MetaTensor"
      ):
          if isinstance(dtype, torch.dtype):
              # already a torch dtype and target `data_type` is torch.Tensor
              return dtype
          return dtype_numpy_to_torch(dtype)
      if not isinstance(dtype, torch.dtype):
          # assuming the dtype is ok if it is not a torch dtype and target `data_type` is not torch.Tensor
          return dtype
      return dtype_torch_to_numpy(dtype)
  
imports: from __future__ import annotations
  import re
  from collections.abc import Sequence
  from typing import Any
  import numpy
  import torch
  import monai
  from monai.config.type_definitions import DtypeLike, NdarrayTensor
  from monai.utils import optional_import
label_desc: Use image node_id 5 for calling a global function with example usage: get_equivalent_dtype(dtype, data_type) and returns: dtype_torch_to_numpy, None, dtype_numpy_to_torch, dtype, dtype