code: def train_step(self, x_train, y_train, log_loss=False):
      # y_train = y_train[:-1]
      # y_train = y_train[1:]
  
      optimizer = self.optimizer
      lossfunc = self.lossfunc
      # Zero the gradients
      self.zero_grad()
  
      # Forward pass
      y_pred = self(x_train)
  
      y_train_len = len(y_train)
      y_pred_len = y_pred.shape[0]
  
      if y_train_len > y_pred_len:
          diff = y_train_len - y_pred_len
          y_train = y_train[diff:]
      elif y_train_len < y_pred_len:
          diff = y_pred_len - y_train_len
          y_pred = y_pred[:-diff, :]
  
      y_train_hot = torch.zeros(len(y_train), self.output_size)
      y_train_hot[range(len(y_train)), y_train] = 1
      y_train_hot = y_train_hot.to("cuda")
  
      # Calculate the loss
      loss = lossfunc(y_pred, y_train_hot)
  
      # Print loss
      if log_loss:
          print("Loss", loss.item())
  
      # Backward pass
      loss.backward()
  
      # Update the weights
      optimizer.step()
  
file_path: TTS/TTS/tts/layers/bark/hubert/tokenizer.py
imports: import json
  import os.path
  from zipfile import ZipFile
  import numpy
  import torch
  from torch import nn, optim
label_desc: Use image node_id 5 for calling the HubertTokenizer obj's underlying member method code with example usage: obj.train_step(x_train, y_train, log_loss) without return types