code: def _prepend_none_dimension(features):
      """Returns a copy of features with adjusted FixedLenSequenceFeature shapes."""
      if features:
          modified_features = dict(features)  # Create a copy to modify
          for key, feature in features.items():
              if isinstance(feature, FixedLenSequenceFeature):
                  if not feature.allow_missing:
                      raise ValueError(
                          "Unsupported: FixedLenSequenceFeature requires "
                          "allow_missing to be True."
                      )
                  modified_features[key] = FixedLenSequenceFeature(
                      [None] + list(feature.shape),
                      feature.dtype,
                      feature.allow_missing,
                      feature.default_value,
                  )
          return modified_features
      else:
          return features
  
imports: from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_assert
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import gen_parsing_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import parsing_config
  from tensorflow.python.ops import parsing_grad
  from tensorflow.python.ops.gen_parsing_ops import *
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 for calling a global function with example usage: _prepend_none_dimension(features) and returns: modified_features, features