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 configure_optimizers(self):
      # Create the optimizer and the training schedule:
      # - Handle the per-param weight decay
      no_decay = ["bias", "LayerNorm.weight"]
      params_decay = [
          p
          for n, p in self.named_parameters()
          if not any(nd in n for nd in no_decay)
      ]
      params_nodecay = [
          p
          for n, p in self.named_parameters()
          if any(nd in n for nd in no_decay)
      ]
      optim_groups = [
          {
              "params": params_decay,
              "weight_decay": self.hparams.weight_decay,
          },
          {"params": params_nodecay, "weight_decay": 0.0},
      ]
  
      # - Start with a warm up, ramp up then cosine
      optimizer = torch.optim.AdamW(
          optim_groups,
          lr=self.hparams.learning_rate,
          betas=self.hparams.betas,
      )
  
      def update_lr(*_):
          config = self.hparams
  
          if self._tokens_seen < config.warmup_tokens:
              # linear warmup
              lr_mult = float(self._tokens_seen) / float(
                  max(1, config.warmup_tokens)
              )
              lr_mult = max(
                  lr_mult, 1e-2
              )  # could be that we've not seen any yet
          else:
              # cosine learning rate decay
              progress = float(
                  self._tokens_seen - config.warmup_tokens
              ) / float(
                  max(1, config.final_tokens - config.warmup_tokens)
              )
              lr_mult = max(
                  0.1, 0.5 * (1.0 + math.cos(math.pi * progress))
              )
  
          return lr_mult
  
      lr_scheduler = {
          "scheduler": torch.optim.lr_scheduler.LambdaLR(
              optimizer,
              lr_lambda=[update_lr, update_lr],
          ),
          "name": "learning_rate",
          "interval": "step",  # The unit of the scheduler's step size
          "frequency": 1,  # The frequency of the scheduler
      }
      return [optimizer], [lr_scheduler]
  
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 4 for calling the GPT obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list, lr_mult