parameters: (self, filenames, compression_type=None, buffer_size=None)
docstring: Creates a `TFRecordDataset`.
  
      Args:
        filenames: A `tf.string` tensor containing one or more filenames.
        compression_type: (Optional.) A `tf.string` scalar evaluating to one of
          `""` (no compression), `"ZLIB"`, or `"GZIP"`.
        buffer_size: (Optional.) A `tf.int64` scalar representing the number of
          bytes in the read buffer. 0 means no buffering.
docstring_summary: Creates a `TFRecordDataset`.
docstring_tokens: Creates a TFRecordDataset .
function: def __init__(self, filenames, compression_type=None, buffer_size=None):
      """Creates a `TFRecordDataset`.
  
      Args:
        filenames: A `tf.string` tensor containing one or more filenames.
        compression_type: (Optional.) A `tf.string` scalar evaluating to one of
          `""` (no compression), `"ZLIB"`, or `"GZIP"`.
        buffer_size: (Optional.) A `tf.int64` scalar representing the number of
          bytes in the read buffer. 0 means no buffering.
      """
      dataset = readers.TFRecordDataset(filenames, compression_type,
                                        buffer_size)
      super(TFRecordDataset, self).__init__(dataset)
function_tokens: def __init__ ( self , filenames , compression_type = None , buffer_size = None ) : dataset = readers . TFRecordDataset ( filenames , compression_type , buffer_size ) super ( TFRecordDataset , self ) . __init__ ( dataset )