class_docstr: Tests for TFRecordObserver class and dataset/spec file functions.
class_docstr_tok: ["Tests","for","TFRecordObserver","class","and","dataset\/spec","file","functions","."]
code: def test_conflicting_specs(self):
      # If two different specs are encountered an error should be thrown
      self.other_data_spec = SimpleSpec(
          step_type=tf.TensorSpec(
              shape=(1), dtype=tf.int32, name="step_type"
          ),
          value=tf.TensorSpec(
              shape=(1, 5), dtype=tf.float64, name="value"
          ),
      )
      self.other_dataset_path = os.path.join(
          self.get_temp_dir(), "other_test_tfrecord_dataset.tfrecord"
      )
      example_encoding_dataset.encode_spec_to_file(
          self.other_dataset_path, self.other_data_spec
      )
      with self.assertRaises(IOError):
          example_encoding_dataset.load_tfrecord_dataset(
              [self.dataset_path, self.other_dataset_path]
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import os
  import numpy
  import tensorflow
  from tf_agents.drivers import dynamic_step_driver
  from tf_agents.drivers import py_driver
  from tf_agents.drivers import test_utils
  from tf_agents.drivers import tf_driver
  from tf_agents.environments import tf_py_environment
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import common
  from tf_agents.utils import eager_utils
  from tf_agents.utils import example_encoding_dataset
  from tf_agents.utils import test_utils
label_desc: Use image node_id 5 for calling the TFRecordsUtilsTest obj's underlying member method code with example usage: obj.test_conflicting_specs() without return types