code: def prepare_tapas_batch_inputs_for_inference():
      # Here we prepare a batch of 2 table-question pairs to test TAPAS inference on:
      data = {
          "Footballer": ["Lionel Messi", "Cristiano Ronaldo"],
          "Age": ["33", "35"],
          "Number of goals": ["712", "750"],
      }
      queries = [
          "Which footballer is 33 years old?",
          "How many goals does Ronaldo have?",
      ]
      table = pd.DataFrame.from_dict(data)
  
      return table, queries
  
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 a global function with example usage: prepare_tapas_batch_inputs_for_inference() and returns: table,  queries