class_docstr: :class:`~transformers.BertConfig` is the configuration class to store the configuration of a
  `BertModel`.
  
  
  Arguments:
      vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
      hidden_size: Size of the encoder layers and the pooler layer.
      num_hidden_layers: Number of hidden layers in the Transformer encoder.
      num_attention_heads: Number of attention heads for each attention layer in
          the Transformer encoder.
      intermediate_size: The size of the "intermediate" (i.e., feed-forward)
          layer in the Transformer encoder.
      hidden_act: The non-linear activation function (function or string) in the
          encoder and pooler. If string, "gelu", "relu", "swish" and "gelu_new" are supported.
      hidden_dropout_prob: The dropout probabilitiy for all fully connected
          layers in the embeddings, encoder, and pooler.
      attention_probs_dropout_prob: The dropout ratio for the attention
          probabilities.
      max_position_embeddings: The maximum sequence length that this model might
          ever be used with. Typically set this to something large just in case
          (e.g., 512 or 1024 or 2048).
      type_vocab_size: The vocabulary size of the `token_type_ids` passed into
          `BertModel`.
      initializer_range: The sttdev of the truncated_normal_initializer for
          initializing all weight matrices.
      layer_norm_eps: The epsilon used by LayerNorm.
code: def __init__(
      self,
      vocab_size_or_config_json_file=30522,
      hidden_size=768,
      num_hidden_layers=12,
      num_attention_heads=12,
      intermediate_size=3072,
      hidden_act="gelu",
      hidden_dropout_prob=0.1,
      attention_probs_dropout_prob=0.1,
      max_position_embeddings=512,
      type_vocab_size=2,
      initializer_range=0.02,
      layer_norm_eps=1e-12,
      output_attentions=False,
      output_hidden_states=False,
  ):
      self.vocab_size = vocab_size_or_config_json_file
      self.hidden_size = hidden_size
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.hidden_act = hidden_act
      self.intermediate_size = intermediate_size
      self.hidden_dropout_prob = hidden_dropout_prob
      self.attention_probs_dropout_prob = attention_probs_dropout_prob
      self.max_position_embeddings = max_position_embeddings
      self.type_vocab_size = type_vocab_size
      self.initializer_range = initializer_range
      self.layer_norm_eps = layer_norm_eps
      self.output_attentions = output_attentions
      self.output_hidden_states = output_hidden_states
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import logging
label_desc: Use image node_id 1 to create a new BertConfig object from inherited base classes: object with example: obj = BertConfig(vocab_size_or_config_json_file, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, output_attentions, output_hidden_states)