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)
  
      accumulators = [
          self._accumulators[self._get_variable_index(variable)].value
          for variable in keras_variables
      ]
      torch._foreach_add_(
          accumulators, torch._foreach_mul(grads, grads)
      )
      torch._foreach_add_(
          variables,
          torch._foreach_div(
              torch._foreach_mul(grads, lr),
              torch._foreach_sqrt(
                  torch._foreach_add(accumulators, self.epsilon)
              ),
          ),
          alpha=-1,
      )
  
file_path: keras/keras/backend/torch/optimizers/torch_adagrad.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 Adagrad obj's underlying member method code with example usage: obj._parallel_update_step(grads, variables, learning_rate) without return types