code: def _parallel_update_step(
      self,
      grads,
      variables,
      learning_rate,
  ):
      keras_variables = variables
      variables = [v.value for v in variables]
      if self.momentum != 0:
          bufs = [
              self.momentums[self._get_variable_index(variable)].value
              for variable in keras_variables
          ]
  
          for i in range(len(bufs)):
              if bufs[i] is None:
                  bufs[i] = torch.clone(grads[i]).detach()
  
          torch._foreach_mul_(bufs, self.momentum)
          torch._foreach_add_(bufs, grads, alpha=-learning_rate)
  
          if self.nesterov:
              torch._foreach_add_(
                  variables, grads, alpha=-learning_rate
              )
              torch._foreach_add_(variables, bufs, alpha=self.momentum)
          else:
              torch._foreach_add_(variables, bufs)
  
      else:
          torch._foreach_add_(variables, grads, alpha=-learning_rate)
  
imports: import torch
  from keras import optimizers
  from keras.backend.torch.optimizers import torch_parallel_optimizer
label_desc: Use image node_id 1 for calling the SGD obj's underlying member method code with example usage: obj._parallel_update_step(grads, variables, learning_rate) without return types