parameters: (file_pattern, batch_size, features,
                                 randomize_input=True, num_epochs=None,
                                 queue_capacity=10000, reader_num_threads=1,
                                 parser_num_threads=1,
                                 name='dequeue_record_examples')
return_statement: return read_batch_features(
        file_pattern=file_pattern, batch_size=batch_size, features=features,
        reader=io_ops.TFRecordReader,
        randomize_input=randomize_input, num_epochs=num_epochs,
        queue_capacity=queue_capacity, reader_num_threads=reader_num_threads,
        parser_num_threads=parser_num_threads, name=name)
docstring: Reads TFRecord, queues, batches and parses `Example` proto.
  
    See more detailed description in `read_examples`.
  
    Args:
      file_pattern: List of files or pattern of file paths containing
          `Example` records. See `tf.gfile.Glob` for pattern rules.
      batch_size: An int or scalar `Tensor` specifying the batch size to use.
      features: A `dict` mapping feature keys to `FixedLenFeature` or
        `VarLenFeature` values.
      randomize_input: Whether the input should be randomized.
      num_epochs: Integer specifying the number of times to read through the
        dataset. If None, cycles through the dataset forever. NOTE - If specified,
        creates a variable that must be initialized, so call
        tf.initialize_local_variables() as shown in the tests.
      queue_capacity: Capacity for input queue.
      reader_num_threads: The number of threads to read examples.
      parser_num_threads: The number of threads to parse examples.
      name: Name of resulting op.
  
    Returns:
      A dict of `Tensor` or `SparseTensor` objects for each in `features`.
  
    Raises:
      ValueError: for invalid inputs.
docstring_summary: Reads TFRecord, queues, batches and parses `Example` proto.
docstring_tokens: Reads TFRecord queues batches and parses Example proto .
function: def read_batch_record_features(file_pattern, batch_size, features,
                                 randomize_input=True, num_epochs=None,
                                 queue_capacity=10000, reader_num_threads=1,
                                 parser_num_threads=1,
                                 name='dequeue_record_examples'):
    """Reads TFRecord, queues, batches and parses `Example` proto.
  
    See more detailed description in `read_examples`.
  
    Args:
      file_pattern: List of files or pattern of file paths containing
          `Example` records. See `tf.gfile.Glob` for pattern rules.
      batch_size: An int or scalar `Tensor` specifying the batch size to use.
      features: A `dict` mapping feature keys to `FixedLenFeature` or
        `VarLenFeature` values.
      randomize_input: Whether the input should be randomized.
      num_epochs: Integer specifying the number of times to read through the
        dataset. If None, cycles through the dataset forever. NOTE - If specified,
        creates a variable that must be initialized, so call
        tf.initialize_local_variables() as shown in the tests.
      queue_capacity: Capacity for input queue.
      reader_num_threads: The number of threads to read examples.
      parser_num_threads: The number of threads to parse examples.
      name: Name of resulting op.
  
    Returns:
      A dict of `Tensor` or `SparseTensor` objects for each in `features`.
  
    Raises:
      ValueError: for invalid inputs.
    """
    return read_batch_features(
        file_pattern=file_pattern, batch_size=batch_size, features=features,
        reader=io_ops.TFRecordReader,
        randomize_input=randomize_input, num_epochs=num_epochs,
        queue_capacity=queue_capacity, reader_num_threads=reader_num_threads,
        parser_num_threads=parser_num_threads, name=name)
function_tokens: def read_batch_record_features ( file_pattern , batch_size , features , randomize_input = True , num_epochs = None , queue_capacity = 10000 , reader_num_threads = 1 , parser_num_threads = 1 , name = 'dequeue_record_examples' ) : return read_batch_features ( file_pattern = file_pattern , batch_size = batch_size , features = features , reader = io_ops . TFRecordReader , randomize_input = randomize_input , num_epochs = num_epochs , queue_capacity = queue_capacity , reader_num_threads = reader_num_threads , parser_num_threads = parser_num_threads , name = name )