code: def train(x, y, model, loss_fn, optim):
      # model returns the prediction and the loss that encourages all experts to have equal importance and load
      y_hat, aux_loss = model(x.float())
      # calculate prediction loss
      loss = loss_fn(y_hat, y)
      # combine losses
      total_loss = loss + aux_loss
      optim.zero_grad()
      total_loss.backward()
      optim.step()
  
      print(
          "Training Results - loss: {:.2f}, aux_loss: {:.3f}".format(
              loss.item(), aux_loss.item()
          )
      )
      return model
  
imports: import torch
  from torch import nn
  from torch.optim import Adam
  from moe import MoE
label_desc: Use image node_id 1 for calling a global function with example usage: train(x, y, model, loss_fn, optim) and returns: model