code: def scatter_update(inputs, indices, updates):
      inputs = convert_to_tensor(inputs)
      indices = convert_to_tensor(indices, dtype="int64")
      updates = convert_to_tensor(updates)
      indices = torch.transpose(indices, 0, 1)
  
      inputs[tuple(indices)] = updates
      return inputs
  
imports: import contextlib
  import os
  import numpy
  import torch
  import tree
  from keras.backend.common import KerasVariable
  from keras.backend.common import global_state
  from keras.backend.common import standardize_dtype
  from keras.backend.common.dtypes import result_type
  from keras.backend.common.keras_tensor import KerasTensor
  from keras.backend.common.stateless_scope import StatelessScope
  from keras.backend.config import floatx
  from keras.utils.nest import pack_sequence_as
label_desc: Use image node_id 14 for calling a global function with example usage: scatter_update(inputs, indices, updates) and returns: inputs