code: def _auto_config(cla55):
      """
      Create the ``Config`` for a class by reflecting on its ``__init__``
      method and applying a few hacks.
      """
      typ3 = _get_config_type(cla55)
  
      # Don't include self, or vocab
      names_to_ignore = set(["self", "vocab"])
  
      # Hack for RNNs
      if cla55 in [torch.nn.RNN, torch.nn.LSTM, torch.nn.GRU]:
          cla55 = torch.nn.RNNBase
          names_to_ignore.add("mode")
  
      if isinstance(cla55, type):
          # It's a class, so inspect its constructor
          function_to_inspect = cla55.__init__
      else:
          # It's a function, so inspect it, and ignore tensor
          function_to_inspect = cla55
          names_to_ignore.add("tensor")
  
      argspec = inspect.getargspec(function_to_inspect)
      comments = _docspec_comments(cla55)
  
      items = []
  
      num_args = len(argspec.args)
      defaults = list(argspec.defaults or [])
      num_default_args = len(defaults)
      num_non_default_args = num_args - num_default_args
  
      # Required args all come first, default args at the end.
      defaults = [
          _NO_DEFAULT for _ in range(num_non_default_args)
      ] + defaults
  
      for name, default in izip(argspec.args, defaults):
          if name in names_to_ignore:
              continue
          annotation = argspec.annotations.get(name)
          comment = comments.get(name)
  
          # Don't include Model, the only place you'd specify that is top-level.
          if annotation == Model:
              continue
  
          # Don't include DataIterator, the only place you'd specify that is top-level.
          if annotation == DataIterator:
              continue
  
          # Don't include params for an Optimizer
          if (
              torch.optim.Optimizer in getattr(cla55, "__bases__", ())
              and name == "params"
          ):
              continue
  
          # Don't include datasets in the trainer
          if cla55 == Trainer and name.endswith("_dataset"):
              continue
  
          # Hack in our Optimizer class to the trainer
          if cla55 == Trainer and annotation == torch.optim.Optimizer:
              annotation = AllenNLPOptimizer
  
          # Hack in embedding num_embeddings as optional (it can be inferred from the pretrained file)
          if cla55 == Embedding and name == "num_embeddings":
              default = None
  
          items.append(ConfigItem(name, annotation, default, comment))
  
      # More hacks, Embedding
      if cla55 == Embedding:
          items.insert(1, ConfigItem("pretrained_file", unicode, None))
  
      return Config(items, typ3=typ3)
  
imports: from __future__ import absolute_import
  from __future__ import print_function
  import collections
  import inspect
  import importlib
  import json
  import re
  import torch
  from numpydoc.docscrape import NumpyDocString
  from allennlp.common import Registrable, JsonDict
  from allennlp.data.iterators import DataIterator
  from allennlp.data.vocabulary import Vocabulary
  from allennlp.models.model import Model
  from allennlp.modules.seq2seq_encoders import _Seq2SeqWrapper
  from allennlp.modules.seq2vec_encoders import _Seq2VecWrapper
  from allennlp.modules.token_embedders import Embedding
  from allennlp.nn.initializers import Initializer
  from allennlp.nn.regularizers import Regularizer
  from allennlp.training.optimizers import Optimizer
  from allennlp.training.trainer import Trainer
label_desc: Use image node_id 6 for calling a global function with example usage: _auto_config(cla55) and returns: Config