parameters: (
          self,
          filenames,
          buffer_size=None,
          num_parallel_reads=None,
          num_parallel_calls=None,
          reader_schema=None,
          deterministic=True,
          block_length=1,
      )
docstring: 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.
docstring_summary: 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.
docstring_tokens: 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 .
function: 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)
function_tokens: def __init__ ( self , filenames , buffer_size = None , num_parallel_reads = None , num_parallel_calls = None , reader_schema = None , deterministic = True , block_length = 1 , ) : _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 )