code: def train(
      gpmodule,
      optimizer=None,
      loss_fn=None,
      retain_graph=None,
      num_steps=1000,
  ):
      """
      A helper to optimize parameters for a GP module.
  
      :param ~pyro.contrib.gp.models.GPModel gpmodule: A GP module.
      :param ~torch.optim.Optimizer optimizer: A PyTorch optimizer instance.
          By default, we use Adam with ``lr=0.01``.
      :param callable loss_fn: A loss function which takes inputs are
          ``gpmodule.model``, ``gpmodule.guide``, and returns ELBO loss.
          By default, ``loss_fn=TraceMeanField_ELBO().differentiable_loss``.
      :param bool retain_graph: An optional flag of ``torch.autograd.backward``.
      :param int num_steps: Number of steps to run SVI.
      :returns: a list of losses during the training procedure
      :rtype: list
      """
      optimizer = (
          torch.optim.Adam(gpmodule.parameters(), lr=0.01)
          if optimizer is None
          else optimizer
      )
      # TODO: add support for JIT loss
      loss_fn = (
          TraceMeanField_ELBO().differentiable_loss
          if loss_fn is None
          else loss_fn
      )
  
      def closure():
          optimizer.zero_grad()
          loss = loss_fn(gpmodule.model, gpmodule.guide)
          torch_backward(loss, retain_graph)
          return loss
  
      losses = []
      for i in range(num_steps):
          loss = optimizer.step(closure)
          losses.append(torch_item(loss))
      return losses
  
imports: import torch
  from pyro.infer import TraceMeanField_ELBO
  from pyro.infer.util import torch_backward, torch_item
label_desc: Use image node_id 2 for calling a global function with example usage: train(gpmodule, optimizer, loss_fn, retain_graph, num_steps) and returns: losses, loss