class_docstr: Configuration for `BertModel`.
class_docstr_tok: ["Configuration","for","`","BertModel","`","."]
docstr: Constructs BertConfig.
  
  Args:
    vocab_size: 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.
    hidden_dropout_prob: The dropout probability 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 stdev of the truncated_normal_initializer for
      initializing all weight matrices.
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code: def __init__(
      self,
      vocab_size,
      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=16,
      initializer_range=0.02,
  ):
      """Constructs BertConfig.
  
      Args:
        vocab_size: 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.
        hidden_dropout_prob: The dropout probability 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 stdev of the truncated_normal_initializer for
          initializing all weight matrices.
      """
      self.vocab_size = vocab_size
      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
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import copy
  import json
  import math
  import re
  import six
  import tensorflow
  from adversarial_filtering.bert import optimization
label_desc: Use image node_id 1 to create a new BertConfig object from inherited base classes: object with example: obj = BertConfig(vocab_size, 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)