class_docstr: A `Dataset` comprising records from one or more text files.
class_docstr_tok: ["A","`","Dataset","`","comprising","records","from","one","or","more","text","files","."]
docstr: Creates a `TextLineDataset`.
  
  Args:
    filenames: A `tf.string` tensor containing one or more filenames.
    compression_type: (Optional.) A `tf.string` scalar evaluating to one of
      `""` (no compression), `"ZLIB"`, or `"GZIP"`.
    buffer_size: (Optional.) A `tf.int64` scalar denoting the number of bytes
      to buffer. A value of 0 results in the default buffering values chosen
      based on the compression type.
    name: (Optional.) A name for the tf.data operation.
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code: def __init__(
      self,
      filenames,
      compression_type=None,
      buffer_size=None,
      name=None,
  ):
      """Creates a `TextLineDataset`.
  
      Args:
        filenames: A `tf.string` tensor containing one or more filenames.
        compression_type: (Optional.) A `tf.string` scalar evaluating to one of
          `""` (no compression), `"ZLIB"`, or `"GZIP"`.
        buffer_size: (Optional.) A `tf.int64` scalar denoting the number of bytes
          to buffer. A value of 0 results in the default buffering values chosen
          based on the compression type.
        name: (Optional.) A name for the tf.data operation.
      """
      self._filenames = filenames
      self._compression_type = convert.optional_param_to_tensor(
          "compression_type",
          compression_type,
          argument_default="",
          argument_dtype=dtypes.string,
      )
      self._buffer_size = convert.optional_param_to_tensor(
          "buffer_size",
          buffer_size,
          argument_default=_DEFAULT_READER_BUFFER_SIZE_BYTES,
      )
      self._name = name
  
      variant_tensor = gen_dataset_ops.text_line_dataset(
          self._filenames,
          self._compression_type,
          self._buffer_size,
          metadata=self._metadata.SerializeToString(),
      )
      super(_TextLineDataset, 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 _TextLineDataset object from inherited base classes: dataset_ops with example: obj = _TextLineDataset(filenames, compression_type, buffer_size, name)