code: def record_batch_to_instance_dicts(
      record_batch: pa.RecordBatch, schema: schema_pb2.Schema
  ) -> List[common_types.InstanceDictType]:
      """Converts pa.RecordBatch to list of Python dictionaries.
  
      Args:
        record_batch: the batch to be converted.
        schema: A `Schema` proto.
  
      Returns:
        A list of dicts where each dict is an in-memory representation of an
            instance.
      """
      # Alternatively, we could've used `record_batch.to_pylist()`, but
      # RaggedTensors would be represented as nested lists (as opposed to array of
      # values + row lengths), so we make a trip through flat examples first.
      coder = example_coder.RecordBatchToExamplesEncoder(schema)
      examples = coder.encode(record_batch)
      # Dense tensor instances must be reshaped according to their spec shape.
      # Scalars are represented as Python scalars (as opposed to singleton arrays).
      feature_spec = schema_utils.schema_as_feature_spec(
          schema
      ).feature_spec
      dense_reshape_fns = {}
  
      def _extract_singleton_item(
          singleton: np.ndarray,
      ) -> common_types.PrimitiveType:
          return singleton.item()
  
      for name, spec in feature_spec.items():
          if isinstance(spec, tf.io.FixedLenFeature):
              if spec.shape:
                  dense_reshape_fns[name] = functools.partial(
                      np.reshape, newshape=spec.shape
                  )
              else:
                  dense_reshape_fns[name] = _extract_singleton_item
      result = []
      for example in examples:
          instance_dict = _example_to_dict(example)
          for name, reshape_fn in dense_reshape_fns.items():
              instance_dict[name] = reshape_fn(instance_dict[name])
          result.append(instance_dict)
      return result
  
imports: import functools
  import os
  import re
  from typing import Callable, Dict, List, Mapping, Optional, FrozenSet
  from absl import logging
  import numpy
  import pyarrow
  import tensorflow
  from tensorflow_transform import analyzer_nodes
  from tensorflow_transform import annotators
  from tensorflow_transform import common_types
  from tensorflow_transform import graph_context
  from tensorflow_transform import graph_tools
  from tensorflow_transform import schema_inference
  from tensorflow_transform import tf2_utils
  from tensorflow_transform import tf_utils
  from tensorflow_transform.output_wrapper import TFTransformOutput
  from tensorflow_transform.saved import saved_transform_io
  from tensorflow_transform.saved import saved_transform_io_v2
  from tensorflow_transform.tf_metadata import dataset_metadata
  from tensorflow_transform.tf_metadata import metadata_io
  from tensorflow_transform.tf_metadata import schema_utils
  from tfx_bsl.coders import example_coder
  from tfx_bsl.tfxio import tensor_to_arrow
  from tensorflow.python.eager import function
  from tensorflow.python.framework import ops
  from tensorflow_metadata.proto.v0 import schema_pb2
label_desc: Use image node_id 7 for calling a global function with example usage: record_batch_to_instance_dicts(record_batch, schema) and returns: result, singleton