code: def _parallel_update_step(
      self,
      grads,
      variables,
      learning_rate,
  ):
      keras_variables = variables
      variables = [v.value for v in variables]
  
      dtype = variables[0].dtype
      lr = ops.cast(learning_rate, dtype)
  
      velocities = [
          self._velocities[self._get_variable_index(variable)].value
          for variable in keras_variables
      ]
  
      rho = self.rho
  
      torch._foreach_mul_(velocities, rho)
      torch._foreach_add_(
          velocities, torch._foreach_mul(grads, grads), alpha=1 - rho
      )
  
      denominators = torch._foreach_add(velocities, self.epsilon)
      if self.centered:
          average_grads = [
              self._average_gradients[
                  self._get_variable_index(variable)
              ].value
              for variable in keras_variables
          ]
          torch._foreach_mul_(average_grads, rho)
          torch._foreach_add_(average_grads, grads, alpha=1 - rho)
          torch._foreach_add_(
              denominators,
              torch._foreach_mul(average_grads, average_grads),
              alpha=-1,
          )
      torch._foreach_sqrt_(denominators)
      increments = torch._foreach_div(
          torch._foreach_mul(grads, lr), denominators
      )
  
      if self.momentum > 0:
          momentum_list = [
              self._momentums[self._get_variable_index(variable)].value
              for variable in keras_variables
          ]
          torch._foreach_mul_(momentum_list, self.momentum)
          torch._foreach_add_(momentum_list, increments)
          torch._foreach_add_(variables, momentum_list, alpha=-1)
      else:
          torch._foreach_add_(variables, increments, alpha=-1)
  
file_path: keras/keras/backend/torch/optimizers/torch_rmsprop.py
imports: import torch
  from keras import ops
  from keras import optimizers
  from keras.backend.torch.optimizers import torch_parallel_optimizer
label_desc: Use image node_id 1 for calling the RMSprop obj's underlying member method code with example usage: obj._parallel_update_step(grads, variables, learning_rate) without return types