code: def _prepend_none_dimension(features):
      """prepend_none_dimension"""
      if features:
          modified_features = dict(features)  # Create a copy to modify
          for key, feature in features.items():
              if isinstance(feature, tf.io.FixedLenFeature):
                  modified_features[key] = tf.io.FixedLenFeature(
                      [None] + list(feature.shape),
                      feature.dtype,
                      feature.default_value,
                  )
          return modified_features
      return features
  
file_path: io/tensorflow_io/python/experimental/parse_avro_ops.py
imports: import collections
  import re
  import tensorflow
  import tensorflow_io
  from tensorflow_io.python.ops import core_ops
label_desc: Use image node_id 3 for calling a global function with example usage: _prepend_none_dimension(features) and returns: features, modified_features