code: def broadcast_np_tensors_to_keras_tensors(
      np_tensors: float | np.ndarray | list[np.ndarray],
      keras_tensors: OptionalList[Tensor],
  ) -> list[np.ndarray]:
      """Broadcasts numpy tensors to the shape of Keras tensors.
  
      :param np_tensors: Numpy tensors that should be broadcasted.
      :type np_tensors: Union[np.ndarray, List[np.ndarray]]
      :param keras_tensors: The Keras tensors with the target shapes.
      :type keras_tensors: OptionalList[Tensor]
      :return: The broadcasted Numpy tensors.
      :rtype: List[np.ndarray]
      """
  
      def none_to_one(shape: ShapeTuple):
          return [1 if dim is None else dim for dim in shape]
  
      keras_tensors = to_list(keras_tensors)
  
      if isinstance(np_tensors, list):
          return [
              np.broadcast_to(n, none_to_one(kbackend.int_shape(k)))
              for k, n in zip(keras_tensors, np_tensors)
          ]
      return [
          np.broadcast_to(
              np_tensors, none_to_one(kbackend.int_shape(k))
          )
          for k in keras_tensors
      ]
  
imports: from __future__ import annotations
  from typing import TypeVar
  import numpy
  import tensorflow
  import tensorflow.keras.backend
  from innvestigate.backend.types import Layer, OptionalList, ShapeTuple, Tensor
label_desc: Use image node_id 14 for calling a global function with example usage: broadcast_np_tensors_to_keras_tensors(np_tensors, keras_tensors) and returns: unknown, unknown, unknown