code: def slicewise(self, fn, *inputs):
      
      if fn == tf.add:
        assert len(inputs) == 2
        if isinstance(inputs[0], mtf.LazyAllreduceSum):
          # sum of LazyAllreduceSum (keep delaying the allreduce)
          return inputs[0] + inputs[1]
      # convert all inputs to LaidOutTensor where possible
      inputs = mtf.convert_args_to_laid_out_tensors(inputs)
      inputs = [x.tensor_list if isinstance(x, self.LaidOutTensor)
                else [x] * len(self.devices) for x in inputs]
      ret = mtf.parallel(self.devices, fn, *inputs)
      if isinstance(ret[0], tuple):
        ret = mtf.transpose_list_of_lists(ret)
        return tuple([self.LaidOutTensor(t) for t in ret])
      else:
        return self.LaidOutTensor(ret)
docs: Execute a function in parallel on all slices.
  
      Args:
        fn: a function from tf.Tensors to tf.Tensor or a tuple of tf.Tensors.
        *inputs: a list of inputs.  Each input is either a LaidOutTensor or
          is convertible to a tf.Tensor.
      Returns:
        a LaidOutTensor, or a tuple of LaidOutTensors if fn returns a tuple.