code: def keras_tensor_from_type_spec(type_spec, name=None):
      """Convert a TypeSpec to a representative KerasTensor."""
      # Create a specialized KerasTensor that supports instance methods,
      # operators, and additional value inference if possible
      keras_tensor_cls = None
      value_type = type_spec.value_type
      for tensor_type, cls in keras_tensor_classes:
          if issubclass(value_type, tensor_type):
              keras_tensor_cls = cls
              break
  
      return keras_tensor_cls.from_type_spec(type_spec, name=name)
  
imports: from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import tensor
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import type_spec
  from tensorflow.python.keras.utils import object_identity
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops.ragged import ragged_operators
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.util import nest
label_desc: Use image node_id 4 for calling a global function with example usage: keras_tensor_from_type_spec(type_spec, name) and returns: keras_tensor_cls