code: def make_golden_dataset(
      compressed_inputs=False,
      mode=tf_estimator.ModeKeys.EVAL,
      use_tpu=False,
  ):
      if compressed_inputs:
          source_path = test_utils.test_tmpfile(
              "make_golden_dataset.tfrecord.gz"
          )
          tfrecord.write_tfrecords(
              tfrecord.read_tfrecords(
                  testdata.GOLDEN_TRAINING_EXAMPLES
              ),
              source_path,
          )
      else:
          source_path = testdata.GOLDEN_TRAINING_EXAMPLES
      return data_providers.get_input_fn_from_filespec(
          input_file_spec=source_path,
          num_examples=testdata.N_GOLDEN_TRAINING_EXAMPLES,
          name="labeled_golden",
          mode=mode,
          tensor_shape=None,
          use_tpu=use_tpu,
      )
  
imports: import math
  from absl.testing import absltest
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow import estimator
  from deepvariant import data_providers
  from deepvariant import dv_config
  from deepvariant import dv_constants
  from deepvariant import dv_utils
  from deepvariant import testdata
  from deepvariant.protos import deepvariant_pb2
  from third_party.nucleus.io import tfrecord
  from third_party.nucleus.testing import test_utils
  from third_party.nucleus.util import variant_utils
  from tensorflow.core.example import example_pb2
label_desc: Use image node_id 2 for calling a global function with example usage: make_golden_dataset(compressed_inputs, mode, use_tpu) and returns: data_providers