code: def train(
      model,
      device,
      train_loader,
      optimizer,
      epoch,
      mode,
      log_interval=10,
  ):
      """The training loop that optimizes the likelihood of a differentable model.
  
      Our model in this example internally unrolls gradient descent
      over an energy function in a differentiable way using higher
      and we can use the outputs of this model just as we use the
      outputs of any other differentiable model to optimize
      a loss function by taking gradient steps.
      """
      model.train()
      for batch_idx, (data, target) in enumerate(train_loader):
          data, target = data.to(device), target.to(device)
          optimizer.zero_grad()
          output = model(data)
          loss = F.cross_entropy(output, target)
          loss.backward()
          optimizer.step()
  
          if mode == "ICNN":
              # In the ICNN, the fce2 weights are required to be non-negative
              # to maintain convexity of the energy function.
              # Here we just project negative weights back onto the
              # non-negative orthant.
              fce2W = model.Enet.state_dict()["fce2.weight"]
              fce2W[fce2W < 0] = 0.0
  
          if batch_idx % log_interval == 0:
              print(
                  "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
                      epoch,
                      batch_idx * len(data),
                      len(train_loader.dataset),
                      100.0 * batch_idx / len(train_loader),
                      loss.item(),
                  )
              )
  
imports: import argparse
  import typing
  import torch
  from torch import nn
  from torch.utils.data import DataLoader
  import torch.nn.functional
  import torch.optim
  from torchvision import datasets, transforms
  import higher
label_desc: Use image node_id 2 for calling a global function with example usage: train(model, device, train_loader, optimizer, epoch, mode, log_interval) without return types