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` to read one or more AvroRecord files.
  Args:
    filenames: A `tf.string` tensor or `tf.data.Dataset` containing one or
      more filenames.
    buffer_size: (Optional.) A `tf.int64` scalar representing the number of
      bytes in the read buffer. If your input pipeline is I/O bottlenecked,
      consider setting this parameter to a value 1-100 MBs. If `None`, a
      sensible default for both local and remote file systems is used.
    num_parallel_reads: (Optional.) A `tf.int64` scalar representing the
      number of files to read in parallel. If greater than one, the records of
      files read in parallel are outputted in an interleaved order. If your
      input pipeline is I/O bottlenecked, consider setting this parameter to a
      value greater than one to parallelize the I/O. If `None`, files will be
      read sequentially. This must be set to equal or greater than `num_parallel_calls`.
      This constraint exists because `num_parallel_reads` becomes `cycle_length` in the
      underlying call to `tf.Dataset.Interleave`, and the `cycle_length` is required to be
      equal or higher than the number of threads(`num_parallel_calls`).
      `cycle_length` in tf.Dataset.Interleave will dictate how many items it will pick up to process
    num_parallel_calls: (Optional.)  number of thread to spawn. This must be set to `None`
      or greater than 0. Also this must be less than or equal to `num_parallel_reads`. This defines
      the degree of parallelism in the underlying Dataset.interleave call.
    reader_schema: (Optional.) A `tf.string` scalar representing the reader
      schema or None.
    deterministic: (Optional.) A boolean controlling whether determinism should be traded for performance by
    allowing elements to be produced out of order. Defaults to `True`
    block_length: Sets the number of output on the output tensor. Defaults to 1
  Raises:
    TypeError: If any argument does not have the expected type.
    ValueError: If any argument does not have the expected shape.
docstr_tok: ["Creates","a","`","AvroRecordDataset","`","to","read","one","or","more","AvroRecord","files",".","Args",":","filenames",":","A","`","tf.string","`","tensor","or","`","tf.data.Dataset","`","containing","one","or","more","filenames",".","buffer_size",":","(","Optional",".",")","A","`","tf.int64","`","scalar","representing","the","number","of","bytes","in","the","read","buffer",".","If","your","input","pipeline","is","I\/O","bottlenecked",",","consider","setting","this","parameter","to","a","value","1-100","MBs",".","If","`","None","`",",","a","sensible","default","for","both","local","and","remote","file","systems","is","used",".","num_parallel_reads",":","(","Optional",".",")","A","`","tf.int64","`","scalar","representing","the","number","of","files","to","read","in","parallel",".","If","greater","than","one",",","the","records","of","files","read","in","parallel","are","outputted","in","an","interleaved","order",".","If","your","input","pipeline","is","I\/O","bottlenecked",",","consider","setting","this","parameter","to","a","value","greater","than","one","to","parallelize","the","I\/O",".","If","`","None","`",",","files","will","be","read","sequentially",".","This","must","be","set","to","equal","or","greater","than","`","num_parallel_calls","`",".","This","constraint","exists","because","`","num_parallel_reads","`","becomes","`","cycle_length","`","in","the","underlying","call","to","`","tf.Dataset.Interleave","`",",","and","the","`","cycle_length","`","is","required","to","be","equal","or","higher","than","the","number","of","threads","(","`","num_parallel_calls","`",")",".","`","cycle_length","`","in","tf.Dataset.Interleave","will","dictate","how","many","items","it","will","pick","up","to","process","num_parallel_calls",":","(","Optional",".",")","number","of","thread","to","spawn",".","This","must","be","set","to","`","None","`","or","greater","than","0",".","Also","this","must","be","less","than","or","equal","to","`","num_parallel_reads","`",".","This","defines","the","degree","of","parallelism","in","the","underlying","Dataset.interleave","call",".","reader_schema",":","(","Optional",".",")","A","`","tf.string","`","scalar","representing","the","reader","schema","or","None",".","deterministic",":","(","Optional",".",")","A","boolean","controlling","whether","determinism","should","be","traded","for","performance","by","allowing","elements","to","be","produced","out","of","order",".","Defaults","to","`","True","`","block_length",":","Sets","the","number","of","output","on","the","output","tensor",".","Defaults","to","1","Raises",":","TypeError",":","If","any","argument","does","not","have","the","expected","type",".","ValueError",":","If","any","argument","does","not","have","the","expected","shape","."]
code: def __init__(
      self,
      filenames,
      buffer_size=None,
      num_parallel_reads=None,
      num_parallel_calls=None,
      reader_schema=None,
      deterministic=True,
      block_length=1,
  ):
      """Creates a `AvroRecordDataset` to read one or more AvroRecord files.
      Args:
        filenames: A `tf.string` tensor or `tf.data.Dataset` containing one or
          more filenames.
        buffer_size: (Optional.) A `tf.int64` scalar representing the number of
          bytes in the read buffer. If your input pipeline is I/O bottlenecked,
          consider setting this parameter to a value 1-100 MBs. If `None`, a
          sensible default for both local and remote file systems is used.
        num_parallel_reads: (Optional.) A `tf.int64` scalar representing the
          number of files to read in parallel. If greater than one, the records of
          files read in parallel are outputted in an interleaved order. If your
          input pipeline is I/O bottlenecked, consider setting this parameter to a
          value greater than one to parallelize the I/O. If `None`, files will be
          read sequentially. This must be set to equal or greater than `num_parallel_calls`.
          This constraint exists because `num_parallel_reads` becomes `cycle_length` in the
          underlying call to `tf.Dataset.Interleave`, and the `cycle_length` is required to be
          equal or higher than the number of threads(`num_parallel_calls`).
          `cycle_length` in tf.Dataset.Interleave will dictate how many items it will pick up to process
        num_parallel_calls: (Optional.)  number of thread to spawn. This must be set to `None`
          or greater than 0. Also this must be less than or equal to `num_parallel_reads`. This defines
          the degree of parallelism in the underlying Dataset.interleave call.
        reader_schema: (Optional.) A `tf.string` scalar representing the reader
          schema or None.
        deterministic: (Optional.) A boolean controlling whether determinism should be traded for performance by
        allowing elements to be produced out of order. Defaults to `True`
        block_length: Sets the number of output on the output tensor. Defaults to 1
      Raises:
        TypeError: If any argument does not have the expected type.
        ValueError: If any argument does not have the expected shape.
      """
      _require(
          num_parallel_calls is None
          or num_parallel_calls == tf.data.experimental.AUTOTUNE
          or num_parallel_calls > 0,
          f"num_parallel_calls: {num_parallel_calls} must be set to None, "
          f"tf.data.experimental.AUTOTUNE, or greater than 0",
      )
      if num_parallel_calls is not None:
          _require(
              num_parallel_reads is not None
              and (
                  num_parallel_reads >= num_parallel_calls
                  or num_parallel_reads == tf.data.experimental.AUTOTUNE
              ),
              f"num_parallel_reads: {num_parallel_reads} must be greater than or equal to "
              f"num_parallel_calls: {num_parallel_calls} or set to tf.data.experimental.AUTOTUNE",
          )
  
      filenames = _create_or_validate_filenames_dataset(filenames)
  
      self._filenames = filenames
      self._buffer_size = buffer_size
      self._num_parallel_reads = num_parallel_reads
      self._num_parallel_calls = num_parallel_calls
      self._reader_schema = reader_schema
      self._block_length = block_length
  
      def read_multiple_files(filenames):
          return _AvroRecordDataset(
              filenames, buffer_size, reader_schema
          )
  
      self._impl = _create_dataset_reader(
          read_multiple_files,
          filenames,
          cycle_length=num_parallel_reads,
          num_parallel_calls=num_parallel_calls,
          deterministic=deterministic,
          block_length=block_length,
      )
      variant_tensor = (
          self._impl._variant_tensor
      )  # pylint: disable=protected-access
      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, num_parallel_reads, num_parallel_calls, reader_schema, deterministic, block_length)