code: def train_step(self, X, y):
      self.module_.train()
  
      # Repackage shared hidden state so that the previous batch
      # does not influence the current one.
      self.hidden = self.repackage_hidden(self.hidden)
      self.module_.zero_grad()
  
      output, self.hidden = self.module_(X, self.hidden)
      y_pred = output.view(-1, self.ntokens)
  
      loss = self.get_loss(y_pred, y)
      loss.backward()
  
      torch.nn.utils.clip_grad_norm_(
          self.module_.parameters(), self.clip
      )
      for p in self.module_.parameters():
          p.data.add_(-self.lr, p.grad.data)
      return {"loss": loss, "y_pred": y_pred}
  
file_path: skorch/examples/word_language_model/net.py
imports: import skorch
  import numpy
  import torch
  from torch.autograd import Variable
  from sklearn.metrics import f1_score
label_desc: Use image node_id 4 for calling the Net obj's underlying member method code with example usage: obj.train_step(X, y) and returns: dict