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 forward(self, src):
      # predict the next tokens (in latent space)
      prediction = self.model(src)
  
      # translate the predictions into tokens
      prediction = self.ln_f(prediction)
      logits = self.head(prediction)
  
      return logits
  
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 5 for calling the GPT obj's underlying member method code with example usage: obj.forward(src) and returns: logits