class_docstr: You can also import this e.g from .test_modeling_tapas import TapasModelTester
class_docstr_tok: ["You","can","also","import","this","e.g","from",".test_modeling_tapas","import","TapasModelTester"]
code: def prepare_config_and_inputs(self):
      input_ids = ids_tensor(
          [self.batch_size, self.seq_length], self.vocab_size
      ).to(torch_device)
  
      input_mask = None
      if self.use_input_mask:
          input_mask = random_attention_mask(
              [self.batch_size, self.seq_length]
          ).to(torch_device)
  
      token_type_ids = []
      for type_vocab_size in self.type_vocab_sizes:
          token_type_ids.append(
              ids_tensor(
                  shape=[self.batch_size, self.seq_length],
                  vocab_size=type_vocab_size,
              )
          )
      token_type_ids = torch.stack(token_type_ids, dim=2).to(
          torch_device
      )
  
      sequence_labels = None
      token_labels = None
      labels = None
      numeric_values = None
      numeric_values_scale = None
      float_answer = None
      aggregation_labels = None
      if self.use_labels:
          sequence_labels = ids_tensor(
              [self.batch_size], self.type_sequence_label_size
          ).to(torch_device)
          token_labels = ids_tensor(
              [self.batch_size, self.seq_length], self.num_labels
          ).to(torch_device)
          labels = ids_tensor(
              [self.batch_size, self.seq_length], vocab_size=2
          ).to(torch_device)
          numeric_values = floats_tensor(
              [self.batch_size, self.seq_length]
          ).to(torch_device)
          numeric_values_scale = floats_tensor(
              [self.batch_size, self.seq_length]
          ).to(torch_device)
          float_answer = floats_tensor([self.batch_size]).to(
              torch_device
          )
          aggregation_labels = ids_tensor(
              [self.batch_size], self.num_aggregation_labels
          ).to(torch_device)
  
      config = self.get_config()
  
      return (
          config,
          input_ids,
          input_mask,
          token_type_ids,
          sequence_labels,
          token_labels,
          labels,
          numeric_values,
          numeric_values_scale,
          float_answer,
          aggregation_labels,
      )
  
imports: import copy
  import unittest
  import numpy
  import pandas
  from transformers import MODEL_FOR_CAUSAL_LM_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, MODEL_FOR_MULTIPLE_CHOICE_MAPPING, MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TapasConfig, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_scatter, require_tensorflow_probability, require_torch, slow, torch_device
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 2 for calling the TapasModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs() and returns: config,  input_ids,  input_mask,  token_type_ids,  sequence_labels,  token_labels,  labels,  numeric_values,  numeric_values_scale,  float_answer,  aggregation_labels