code: def test_flatten(self):
      _, row_index, col_index = self._prepare_tables()
      row_index_flat = flatten(row_index)
      col_index_flat = flatten(col_index)
  
      shape = [3, 4, 5]
      batched_index = IndexMap(
          indices=torch.zeros(shape).type(torch.LongTensor),
          num_segments=1,
          batch_dims=3,
      )
      batched_index_flat = flatten(batched_index)
  
      # We use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
      np.testing.assert_array_equal(
          row_index_flat.indices.numpy(),
          [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5],
      )
      np.testing.assert_array_equal(
          col_index_flat.indices.numpy(),
          [0, 0, 1, 0, 0, 1, 0, 0, 1, 3, 4, 5, 3, 4, 5, 3, 4, 5],
      )
      self.assertEqual(
          batched_index_flat.num_segments.numpy(), np.prod(shape)
      )
      np.testing.assert_array_equal(
          batched_index_flat.indices.numpy(), range(np.prod(shape))
      )
  
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 3 for calling the TapasUtilitiesTest obj's underlying member method code with example usage: obj.test_flatten() without return types