class_docstr: A `Dataset` comprising records from one or more AvroRecord files.
class_docstr_tok: ["A","`","Dataset","`","comprising","records","from","one","or","more","AvroRecord","files","."]
docstr: Creates a `AvroRecordDataset`.
  
  Args:
    filenames: A `tf.string` tensor containing one or more filenames.
    buffer_size: (Optional.) A `tf.int64` scalar representing the number of
      bytes in the read buffer. 0 means no buffering.
    reader_schema: (Optional.) A `tf.string` scalar
    representing the reader schema or None
docstr_tok: ["Creates","a","`","AvroRecordDataset","`",".","Args",":","filenames",":","A","`","tf.string","`","tensor","containing","one","or","more","filenames",".","buffer_size",":","(","Optional",".",")","A","`","tf.int64","`","scalar","representing","the","number","of","bytes","in","the","read","buffer",".","0","means","no","buffering",".","reader_schema",":","(","Optional",".",")","A","`","tf.string","`","scalar","representing","the","reader","schema","or","None"]
code: def __init__(self, filenames, buffer_size=None, reader_schema=None):
      """Creates a `AvroRecordDataset`.
  
      Args:
        filenames: A `tf.string` tensor containing one or more filenames.
        buffer_size: (Optional.) A `tf.int64` scalar representing the number of
          bytes in the read buffer. 0 means no buffering.
        reader_schema: (Optional.) A `tf.string` scalar
        representing the reader schema or None
      """
      self._filenames = filenames
      self._buffer_size = _AvroRecordDataset.__optional_param_to_tensor(
          "buffer_size",
          buffer_size,
          argument_default=_DEFAULT_READER_BUFFER_SIZE_BYTES,
      )
      self._reader_schema = (
          _AvroRecordDataset.__optional_param_to_tensor(
              "reader_schema",
              reader_schema,
              argument_default=_DEFAULT_READER_SCHEMA,
              argument_dtype=tf.dtypes.string,
          )
      )
      variant_tensor = core_ops.io_avro_record_dataset(
          self._filenames, self._buffer_size, self._reader_schema
      )
      super().__init__(variant_tensor)
  
file_path: io/tensorflow_io/python/experimental/avro_record_dataset_ops.py
imports: import tensorflow
  from tensorflow_io.python.ops import core_ops
label_desc: Use image node_id 1 to create a new _AvroRecordDataset object from inherited base classes: tf.data with example: obj = _AvroRecordDataset(filenames, buffer_size, reader_schema)