code: def _record_batch_to_extracts(record_batch):
      input_index = record_batch.schema.names.index(
          constants.ARROW_INPUT_COLUMN
      )
      return {
          constants.FEATURES_KEY: tfma_util.record_batch_to_tensor_values(
              record_batch
          ),
          constants.INPUT_KEY: np.asarray(
              record_batch.columns[input_index].flatten()
          ),
      }
  
imports: import tempfile
  import unittest
  from absl.testing import parameterized
  import apache_beam
  from apache_beam.testing import util
  import numpy
  import tensorflow
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.eval_saved_model import testutil
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.utils import model_util
  from tensorflow_model_analysis.utils import util
  from tfx_bsl.tfxio import tf_example_record
  from google.protobuf import text_format
  from tensorflow.core.protobuf import saved_model_pb2
  from tensorflow_metadata.proto.v0 import schema_pb2
label_desc: Use image node_id 1 for calling a global function with example usage: _record_batch_to_extracts(record_batch) and returns: dict