code: def tf_records_empty():
      """Underlying data corresponds to `data_empty` fixture."""
      import tensorflow as tf
  
      return [
          # Record one (corresponding to row one).
          tf.train.Example(
              features=tf.train.Features(
                  feature={
                      "int_item": tf.train.Feature(
                          int64_list=tf.train.Int64List(value=[1])
                      ),
                      "int_list": tf.train.Feature(
                          int64_list=tf.train.Int64List(value=[2, 2, 3])
                      ),
                      "int_partial": tf.train.Feature(
                          int64_list=tf.train.Int64List(value=[])
                      ),
                      "int_empty": tf.train.Feature(
                          int64_list=tf.train.Int64List(value=[])
                      ),
                      "float_item": tf.train.Feature(
                          float_list=tf.train.FloatList(value=[1.0])
                      ),
                      "float_list": tf.train.Feature(
                          float_list=tf.train.FloatList(
                              value=[2.0, 3.0, 4.0]
                          )
                      ),
                      "float_partial": tf.train.Feature(
                          float_list=tf.train.FloatList(value=[1.0])
                      ),
                      "float_empty": tf.train.Feature(
                          float_list=tf.train.FloatList(value=[])
                      ),
                      "bytes_item": tf.train.Feature(
                          bytes_list=tf.train.BytesList(value=[b"abc"])
                      ),
                      "bytes_list": tf.train.Feature(
                          bytes_list=tf.train.BytesList(
                              value=[b"def", b"1234"]
                          )
                      ),
                      "bytes_partial": tf.train.Feature(
                          bytes_list=tf.train.BytesList(value=[])
                      ),
                      "bytes_empty": tf.train.Feature(
                          bytes_list=tf.train.BytesList(value=[])
                      ),
                      "string_item": tf.train.Feature(
                          bytes_list=tf.train.BytesList(value=[b"uvw"])
                      ),
                      "string_list": tf.train.Feature(
                          bytes_list=tf.train.BytesList(
                              value=[b"xyz", b"999"]
                          )
                      ),
                      "string_partial": tf.train.Feature(
                          bytes_list=tf.train.BytesList(value=[])
                      ),
                      "string_empty": tf.train.Feature(
                          bytes_list=tf.train.BytesList(value=[])
                      ),
                  }
              )
          ),
          # Record two (corresponding to row two).
          tf.train.Example(
              features=tf.train.Features(
                  feature={
                      "int_item": tf.train.Feature(
                          int64_list=tf.train.Int64List(value=[2])
                      ),
                      "int_list": tf.train.Feature(
                          int64_list=tf.train.Int64List(value=[3, 3, 4])
                      ),
                      "int_partial": tf.train.Feature(
                          int64_list=tf.train.Int64List(value=[9, 2])
                      ),
                      "int_empty": tf.train.Feature(
                          int64_list=tf.train.Int64List(value=[])
                      ),
                      "float_item": tf.train.Feature(
                          float_list=tf.train.FloatList(value=[2.0])
                      ),
                      "float_list": tf.train.Feature(
                          float_list=tf.train.FloatList(
                              value=[5.0, 6.0, 7.0]
                          )
                      ),
                      "float_partial": tf.train.Feature(
                          float_list=tf.train.FloatList(value=[])
                      ),
                      "float_empty": tf.train.Feature(
                          float_list=tf.train.FloatList(value=[])
                      ),
                      "bytes_item": tf.train.Feature(
                          bytes_list=tf.train.BytesList(value=[b"ghi"])
                      ),
                      "bytes_list": tf.train.Feature(
                          bytes_list=tf.train.BytesList(
                              value=[b"jkl", b"5678"]
                          )
                      ),
                      "bytes_partial": tf.train.Feature(
                          bytes_list=tf.train.BytesList(
                              value=[b"hello"]
                          )
                      ),
                      "bytes_empty": tf.train.Feature(
                          bytes_list=tf.train.BytesList(value=[])
                      ),
                      "string_item": tf.train.Feature(
                          bytes_list=tf.train.BytesList(value=[b"mno"])
                      ),
                      "string_list": tf.train.Feature(
                          bytes_list=tf.train.BytesList(
                              value=[b"pqr", b"111"]
                          )
                      ),
                      "string_partial": tf.train.Feature(
                          bytes_list=tf.train.BytesList(
                              value=[b"world"]
                          )
                      ),
                      "string_empty": tf.train.Feature(
                          bytes_list=tf.train.BytesList(value=[])
                      ),
                  }
              )
          ),
      ]
  
imports: import json
  import os
  from typing import TYPE_CHECKING
  import numpy
  import pytest
  from pandas.api.types import is_float_dtype, is_int64_dtype, is_object_dtype
  import ray
  from ray.tests.conftest import *
label_desc: Use image node_id 3 for calling a global function with example usage: tf_records_empty() and returns: list