class_docstr: A `Dataset` comprising records from one or more TFRecord files.
  
  This dataset loads TFRecords from the files as bytes, exactly as they were
  written.`TFRecordDataset` does not do any parsing or decoding on its own.
  Parsing and decoding can be done by applying `Dataset.map` transformations
  after the `TFRecordDataset`.
  
  A minimal example is given below:
  
  >>> import tempfile
  >>> example_path = os.path.join(tempfile.gettempdir(), "example.tfrecords")
  >>> np.random.seed(0)
  
  >>> # Write the records to a file.
  ... with tf.io.TFRecordWriter(example_path) as file_writer:
  ...   for _ in range(4):
  ...     x, y = np.random.random(), np.random.random()
  ...
  ...     record_bytes = tf.train.Example(features=tf.train.Features(feature={
  ...         "x": tf.train.Feature(float_list=tf.train.FloatList(value=[x])),
  ...         "y": tf.train.Feature(float_list=tf.train.FloatList(value=[y])),
  ...     })).SerializeToString()
  ...     file_writer.write(record_bytes)
  
  >>> # Read the data back out.
  >>> def decode_fn(record_bytes):
  ...   return tf.io.parse_single_example(
  ...       # Data
  ...       record_bytes,
  ...
  ...       # Schema
  ...       {"x": tf.io.FixedLenFeature([], dtype=tf.float32),
  ...        "y": tf.io.FixedLenFeature([], dtype=tf.float32)}
  ...   )
  
  >>> for batch in tf.data.TFRecordDataset([example_path]).map(decode_fn):
  ...   print("x = {x:.4f},  y = {y:.4f}".format(**batch))
  x = 0.5488,  y = 0.7152
  x = 0.6028,  y = 0.5449
  x = 0.4237,  y = 0.6459
  x = 0.4376,  y = 0.8918
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code: def element_spec(self):
      return tensor_spec.TensorSpec([], dtypes.string)
  
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 3 for calling the TFRecordDatasetV2 obj's underlying member method code with example usage: obj.element_spec() and returns: tensor_spec