code: def vectorized_map(function, elements):
      """Parallel map of `function` on axis 0 of tensor(s) `elements`.
  
      Schematically, `vectorized_map` implements the following,
      in the case of a single tensor input `elements`:
  
      ```python
      def vectorized_map(function, elements)
          outputs = []
          for e in elements:
              outputs.append(function(e))
          return stack(outputs)
      ```
  
      In the case of an iterable of tensors `elements`,
      it implements the following:
  
      ```python
      def vectorized_map(function, elements)
          batch_size = elements[0].shape[0]
          outputs = []
          for index in range(batch_size):
              outputs.append(function([e[index] for e in elements]))
          return np.stack(outputs)
      ```
  
      In this case, `function` is expected to take as input
      a single list of tensor arguments.
      """
      return backend.core.vectorized_map(function, elements)
  
imports: import numpy
  from keras import backend
  from keras.api_export import keras_export
  from keras.backend import KerasTensor
  from keras.backend import any_symbolic_tensors
  from keras.ops.operation import Operation
  from keras.utils import traceback_utils
label_desc: Use image node_id 14 for calling a global function with example usage: vectorized_map(function, elements) and returns: backend