code: def gradient_update_parameters(
      model, loss, params=None, step_size=0.5, first_order=False
  ):
      """Update of the meta-parameters with one step of gradient descent on the
      loss function.
  
      Parameters
      ----------
      model : `torchmeta.modules.MetaModule` instance
          The model.
  
      loss : `torch.Tensor` instance
          The value of the inner-loss. This is the result of the training dataset
          through the loss function.
  
      params : `collections.OrderedDict` instance, optional
          Dictionary containing the meta-parameters of the model. If `None`, then
          the values stored in `model.meta_named_parameters()` are used. This is
          useful for running multiple steps of gradient descent as the inner-loop.
  
      step_size : int, `torch.Tensor`, or `collections.OrderedDict` instance (default: 0.5)
          The step size in the gradient update. If an `OrderedDict`, then the
          keys must match the keys in `params`.
  
      first_order : bool (default: `False`)
          If `True`, then the first order approximation of MAML is used.
  
      Returns
      -------
      updated_params : `collections.OrderedDict` instance
          Dictionary containing the updated meta-parameters of the model, with one
          gradient update wrt. the inner-loss.
      """
      if not isinstance(model, MetaModule):
          raise ValueError(
              "The model must be an instance of `torchmeta.modules."
              "MetaModule`, got `{0}`".format(type(model))
          )
  
      if params is None:
          params = OrderedDict(model.meta_named_parameters())
  
      grads = torch.autograd.grad(
          loss, params.values(), create_graph=not first_order
      )
  
      updated_params = OrderedDict()
  
      if isinstance(step_size, (dict, OrderedDict)):
          for (name, param), grad in zip(params.items(), grads):
              updated_params[name] = param - step_size[name] * grad
  
      else:
          for (name, param), grad in zip(params.items(), grads):
              updated_params[name] = param - step_size * grad
  
      return updated_params
  
label_desc: Use image node_id 1 for calling a global function with example usage: gradient_update_parameters(model, loss, params, step_size, first_order) and returns: updated_params