class_docstr: CrossModel for DRL.
class_docstr_tok: ["CrossModel","for","DRL","."]
docstr: :obj:`torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).
docstr_tok: [":","obj",":","`","torch.dtype","`",":","The","dtype","of","the","module","(","assuming","that","all","the","module","parameters","have","the","same","dtype",")","."]
code: def dtype(self):
      """
      :obj:`torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).
      """
      try:
          return next(self.parameters()).dtype
      except StopIteration:
          # For nn.DataParallel compatibility in PyTorch 1.5
          def find_tensor_attributes(module: nn.Module):
              tuples = [
                  (k, v)
                  for k, v in module.__dict__.items()
                  if torch.is_tensor(v)
              ]
              return tuples
  
          gen = self._named_members(
              get_members_fn=find_tensor_attributes
          )
          first_tuple = next(gen)
          return first_tuple[1].dtype
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import logging
  import torch
  from torch import nn
  from towhee.models.drl.until_module import LayerNorm
  from collections import OrderedDict
label_desc: Use image node_id 5 for calling the CrossModel obj's underlying member method code with example usage: obj.dtype() and returns: next, first_tuple, tuples