class_docstr: Base class for PyTorch meta-learning modules. These modules accept an
  additional argument `params` in their `forward` method.
  
  Notes
  -----
  Objects inherited from `MetaModule` are fully compatible with PyTorch
  modules from `torch.nn.Module`. The argument `params` is a dictionary of
  tensors, with full support of the computation graph (for differentiation).
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code: def meta_named_parameters(self, prefix="", recurse=True):
      gen = self._named_members(
          lambda module: module._parameters.items()
          if isinstance(module, MetaModule)
          else [],
          prefix=prefix,
          recurse=recurse,
      )
      for elem in gen:
          yield elem
  
file_path: pytorch-meta/torchmeta/modules/module.py
imports: import torch
  import torch.nn
  import re
  import warnings
  from collections import OrderedDict
label_desc: Use image node_id 2 for calling the MetaModule obj's underlying member method code with example usage: obj.meta_named_parameters(prefix, recurse) without return types