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)
      local_step = ops.cast(self.iterations + 1, dtype)
  
      beta_1_power = ops.power(ops.cast(self.beta_1, dtype), local_step)
      beta_2_power = ops.power(ops.cast(self.beta_2, dtype), local_step)
      alpha = lr * ops.sqrt(1 - beta_2_power) / (1 - beta_1_power)
  
      m_list = [
          self._momentums[self._get_variable_index(variable)].value
          for variable in keras_variables
      ]
      v_list = [
          self._velocities[self._get_variable_index(variable)].value
          for variable in keras_variables
      ]
  
      torch._foreach_mul_(m_list, self.beta_1)
      torch._foreach_add_(m_list, grads, alpha=1 - self.beta_1)
  
      torch._foreach_mul_(v_list, self.beta_2)
      torch._foreach_add_(
          v_list,
          torch._foreach_mul(grads, grads),
          alpha=1 - self.beta_2,
      )
  
      if self.amsgrad:
          v_hat_list = [
              self._velocity_hats[
                  self._get_variable_index(variable)
              ].value
              for variable in keras_variables
          ]
          torch._foreach_maximum_(v_hat_list, v_list)
          v_list = v_hat_list
  
      torch._foreach_add_(
          variables,
          torch._foreach_div(
              torch._foreach_mul(m_list, alpha),
              torch._foreach_add(
                  torch._foreach_sqrt(v_list), self.epsilon
              ),
          ),
          alpha=-1,
      )
  
file_path: keras/keras/backend/torch/optimizers/torch_adam.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 Adam obj's underlying member method code with example usage: obj._parallel_update_step(grads, variables, learning_rate) without return types