class_docstr: the full GPT language model, with a context size of block_size
class_docstr_tok: ["the","full","GPT","language","model",",","with","a","context","size","of","block_size"]
code: def training_step(self, batch, _):
      src, targets = batch
  
      # Update the tokens we've seen (tracked for LR scheduling)
      self._tokens_seen += (src >= 0).numel()
  
      # same action as inference
      logits = self(src)
  
      # if we are given some desired targets also calculate the loss
      loss = None
      if targets is not None:
          loss = F.cross_entropy(
              logits.view(-1, logits.size(-1)), targets.view(-1)
          )
  
      self.logger.log_metrics(
          {
              "train_loss": loss.mean(),
              "learning_rate": self.lr_schedulers().get_last_lr()[0],
          },
          step=trainer.global_step,
      )
  
      return loss
  
imports: import math
  import os
  import pytorch_lightning
  import torch
  import torch.nn
  from pytorch_lightning import Trainer, seed_everything
  from pytorch_lightning.utilities import rank_zero_info
  from torch.nn import functional
  from torch.utils.data import DataLoader, Dataset, RandomSampler
  from xformers.factory.model_factory import xFormer, xFormerConfig
label_desc: Use image node_id 6 for calling the GPT obj's underlying member method code with example usage: obj.training_step(batch, _) and returns: loss