code: def test_batch_predictions_are_consistent(self):
      # The CNN encoder has problems with this kind of test - it's not properly masked yet, so
      # changing the amount of padding in the batch will result in small differences in the
      # output of the encoder.  Because BiDAF is so deep, these differences get magnified through
      # the network and make this test impossible.  So, we'll remove the CNN encoder entirely
      # from the model for this test.  If/when we fix the CNN encoder to work correctly with
      # masking, we can change this back to how the other models run this test, with just a
      # single line.
      # pylint: disable=protected-access,attribute-defined-outside-init
  
      # Save some state.
      saved_model = self.model
      saved_instances = self.instances
  
      # Modify the state, run the test with modified state.
      params = Params.from_file(self.param_file)
      reader = DatasetReader.from_params(params["dataset_reader"])
      reader._token_indexers = {
          "tokens": reader._token_indexers["tokens"]
      }
      self.instances = reader.read(
          self.FIXTURES_ROOT / "data" / "squad.json"
      )
      vocab = Vocabulary.from_instances(self.instances)
      for instance in self.instances:
          instance.index_fields(vocab)
      del params["model"]["text_field_embedder"]["token_embedders"][
          "token_characters"
      ]
      params["model"]["phrase_layer"]["input_size"] = 2
      self.model = Model.from_params(
          vocab=vocab, params=params["model"]
      )
  
      self.ensure_batch_predictions_are_consistent()
  
      # Restore the state.
      self.model = saved_model
      self.instances = saved_instances
  
imports: from __future__ import with_statement
  from __future__ import division
  from __future__ import absolute_import
  from flaky import flaky
  import pytest
  import numpy
  from numpy.testing import assert_almost_equal
  import torch
  from allennlp.common import Params
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import ModelTestCase
  from allennlp.data import DatasetReader, Vocabulary
  from allennlp.data.dataset import Batch
  from allennlp.models import BidirectionalAttentionFlow, Model
label_desc: Use image node_id 4 for calling the BidirectionalAttentionFlowTest obj's underlying member method code with example usage: obj.test_batch_predictions_are_consistent() without return types