class_docstr: A `Dataset` of fixed-length records from one or more binary files.
class_docstr_tok: ["A","`","Dataset","`","of","fixed-length","records","from","one","or","more","binary","files","."]
docstr: Creates a `FixedLengthRecordDataset`.
  
  Args:
    filenames: A `tf.string` tensor containing one or more filenames.
    record_bytes: A `tf.int64` scalar representing the number of bytes in each
      record.
    header_bytes: (Optional.) A `tf.int64` scalar representing the number of
      bytes to skip at the start of a file.
    footer_bytes: (Optional.) A `tf.int64` scalar representing the number of
      bytes to ignore at the end of a file.
    buffer_size: (Optional.) A `tf.int64` scalar representing the number of
      bytes to buffer when reading.
    compression_type: (Optional.) A `tf.string` scalar evaluating to one of
      `""` (no compression), `"ZLIB"`, or `"GZIP"`.
    name: (Optional.) A name for the tf.data operation.
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code: def __init__(
      self,
      filenames,
      record_bytes,
      header_bytes=None,
      footer_bytes=None,
      buffer_size=None,
      compression_type=None,
      name=None,
  ):
      """Creates a `FixedLengthRecordDataset`.
  
      Args:
        filenames: A `tf.string` tensor containing one or more filenames.
        record_bytes: A `tf.int64` scalar representing the number of bytes in each
          record.
        header_bytes: (Optional.) A `tf.int64` scalar representing the number of
          bytes to skip at the start of a file.
        footer_bytes: (Optional.) A `tf.int64` scalar representing the number of
          bytes to ignore at the end of a file.
        buffer_size: (Optional.) A `tf.int64` scalar representing the number of
          bytes to buffer when reading.
        compression_type: (Optional.) A `tf.string` scalar evaluating to one of
          `""` (no compression), `"ZLIB"`, or `"GZIP"`.
        name: (Optional.) A name for the tf.data operation.
      """
      self._filenames = filenames
      self._record_bytes = ops.convert_to_tensor(
          record_bytes, dtype=dtypes.int64, name="record_bytes"
      )
      self._header_bytes = convert.optional_param_to_tensor(
          "header_bytes", header_bytes
      )
      self._footer_bytes = convert.optional_param_to_tensor(
          "footer_bytes", footer_bytes
      )
      self._buffer_size = convert.optional_param_to_tensor(
          "buffer_size", buffer_size, _DEFAULT_READER_BUFFER_SIZE_BYTES
      )
      self._compression_type = convert.optional_param_to_tensor(
          "compression_type",
          compression_type,
          argument_default="",
          argument_dtype=dtypes.string,
      )
      self._name = name
  
      variant_tensor = gen_dataset_ops.fixed_length_record_dataset_v2(
          self._filenames,
          self._header_bytes,
          self._record_bytes,
          self._footer_bytes,
          self._buffer_size,
          self._compression_type,
          metadata=self._metadata.SerializeToString(),
      )
      super(_FixedLengthRecordDataset, self).__init__(variant_tensor)
  
imports: import os
  from tensorflow.python import tf2
  from tensorflow.python.compat import v2_compat
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import from_tensor_slices_op
  from tensorflow.python.data.ops import structured_function
  from tensorflow.python.data.util import convert
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.framework import type_spec
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_dataset_ops
  from tensorflow.python.ops import gen_experimental_dataset_ops
  from tensorflow.python.types import data
  from tensorflow.python.util import nest
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 to create a new _FixedLengthRecordDataset object from inherited base classes: dataset_ops with example: obj = _FixedLengthRecordDataset(filenames, record_bytes, header_bytes, footer_bytes, buffer_size, compression_type, name)