code: def __init__(
      self,
      vocab_size=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=1024,
      type_vocab_sizes=[3, 256, 256, 2, 256, 256, 10],
      initializer_range=0.02,
      layer_norm_eps=1e-12,
      pad_token_id=0,
      positive_label_weight=10.0,
      num_aggregation_labels=0,
      aggregation_loss_weight=1.0,
      use_answer_as_supervision=None,
      answer_loss_importance=1.0,
      use_normalized_answer_loss=False,
      huber_loss_delta=None,
      temperature=1.0,
      aggregation_temperature=1.0,
      use_gumbel_for_cells=False,
      use_gumbel_for_aggregation=False,
      average_approximation_function="ratio",
      cell_selection_preference=None,
      answer_loss_cutoff=None,
      max_num_rows=64,
      max_num_columns=32,
      average_logits_per_cell=False,
      select_one_column=True,
      allow_empty_column_selection=False,
      init_cell_selection_weights_to_zero=False,
      reset_position_index_per_cell=True,
      disable_per_token_loss=False,
      aggregation_labels=None,
      no_aggregation_label_index=None,
      **kwargs,
  ):
      super().__init__(pad_token_id=pad_token_id, **kwargs)
  
      # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
      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_sizes = type_vocab_sizes
      self.initializer_range = initializer_range
      self.layer_norm_eps = layer_norm_eps
  
      # Fine-tuning task hyperparameters
      self.positive_label_weight = positive_label_weight
      self.num_aggregation_labels = num_aggregation_labels
      self.aggregation_loss_weight = aggregation_loss_weight
      self.use_answer_as_supervision = use_answer_as_supervision
      self.answer_loss_importance = answer_loss_importance
      self.use_normalized_answer_loss = use_normalized_answer_loss
      self.huber_loss_delta = huber_loss_delta
      self.temperature = temperature
      self.aggregation_temperature = aggregation_temperature
      self.use_gumbel_for_cells = use_gumbel_for_cells
      self.use_gumbel_for_aggregation = use_gumbel_for_aggregation
      self.average_approximation_function = (
          average_approximation_function
      )
      self.cell_selection_preference = cell_selection_preference
      self.answer_loss_cutoff = answer_loss_cutoff
      self.max_num_rows = max_num_rows
      self.max_num_columns = max_num_columns
      self.average_logits_per_cell = average_logits_per_cell
      self.select_one_column = select_one_column
      self.allow_empty_column_selection = allow_empty_column_selection
      self.init_cell_selection_weights_to_zero = (
          init_cell_selection_weights_to_zero
      )
      self.reset_position_index_per_cell = reset_position_index_per_cell
      self.disable_per_token_loss = disable_per_token_loss
  
      # Aggregation hyperparameters
      self.aggregation_labels = aggregation_labels
      self.no_aggregation_label_index = no_aggregation_label_index
  
      if isinstance(self.aggregation_labels, dict):
          self.aggregation_labels = {
              int(k): v for k, v in aggregation_labels.items()
          }
  
imports: from ...configuration_utils import PretrainedConfig
label_desc: Use image node_id 1 to create a new TapasConfig object from inherited base classes: PretrainedConfig with example: obj = TapasConfig(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_sizes, initializer_range, layer_norm_eps, pad_token_id, positive_label_weight, num_aggregation_labels, aggregation_loss_weight, use_answer_as_supervision, answer_loss_importance, use_normalized_answer_loss, huber_loss_delta, temperature, aggregation_temperature, use_gumbel_for_cells, use_gumbel_for_aggregation, average_approximation_function, cell_selection_preference, answer_loss_cutoff, max_num_rows, max_num_columns, average_logits_per_cell, select_one_column, allow_empty_column_selection, init_cell_selection_weights_to_zero, reset_position_index_per_cell, disable_per_token_loss, aggregation_labels, no_aggregation_label_index)