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)
  
      m_list = [
          self._m[self._get_variable_index(variable)].value
          for variable in keras_variables
      ]
      u_list = [
          self._u[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_(u_list, self.beta_2)
      torch._foreach_maximum_(u_list, torch._foreach_abs(grads))
  
      torch._foreach_add_(
          variables,
          torch._foreach_div(
              torch._foreach_mul(m_list, lr),
              torch._foreach_mul(
                  torch._foreach_add(u_list, self.epsilon),
                  1 - beta_1_power,
              ),
          ),
          alpha=-1,
      )
  
file_path: keras/keras/backend/torch/optimizers/torch_adamax.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 Adamax obj's underlying member method code with example usage: obj._parallel_update_step(grads, variables, learning_rate) without return types