code: def slice_update(inputs, start_indices, updates):
      shape_dtype = to_torch_dtype("int64")
      inputs = convert_to_tensor(inputs)
      start_indices = convert_to_tensor(start_indices).to(shape_dtype)
      updates = convert_to_tensor(updates)
  
      python_slice = __builtins__["slice"]
      slices = [
          python_slice(start_index, start_index + update_length)
          for start_index, update_length in zip(
              start_indices, updates.shape
          )
      ]
      outputs = torch.clone(inputs)
      outputs[slices] = updates
      return outputs
  
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 16 for calling a global function with example usage: slice_update(inputs, start_indices, updates) and returns: outputs