parameters: (self, filenames, compression_type=None)
docstring: Creates a `TFRecordDataset`.
  
      Args:
        filenames: A `tf.string` tensor containing one or more filenames.
        compression_type: A `tf.string` scalar evaluating to one of `""` (no
          compression), `"ZLIB"`, or `"GZIP"`.
docstring_summary: Creates a `TFRecordDataset`.
docstring_tokens: Creates a TFRecordDataset .
function: def __init__(self, filenames, compression_type=None):
      """Creates a `TFRecordDataset`.
  
      Args:
        filenames: A `tf.string` tensor containing one or more filenames.
        compression_type: A `tf.string` scalar evaluating to one of `""` (no
          compression), `"ZLIB"`, or `"GZIP"`.
      """
      super(TFRecordDataset, self).__init__()
      self._filenames = ops.convert_to_tensor(filenames, name="filenames")
      if compression_type is not None:
        self._compression_type = ops.convert_to_tensor(
            compression_type, dtype=dtypes.string, name="compression_type")
      else:
        self._compression_type = constant_op.constant("", name="compression_type")
function_tokens: def __init__ ( self , filenames , compression_type = None ) : super ( TFRecordDataset , self ) . __init__ ( ) self . _filenames = ops . convert_to_tensor ( filenames , name = "filenames" ) if compression_type is not None : self . _compression_type = ops . convert_to_tensor ( compression_type , dtype = dtypes . string , name = "compression_type" ) else : self . _compression_type = constant_op . constant ( "" , name = "compression_type" )