code: def forward(
      self,
      idx,
      merge_context=False,
      past_kv=None,
      position_ids=None,
      use_cache=False,
  ):
      device = idx.device
      b, t = idx.size()
      if past_kv is not None:
          assert t == 1
          tok_emb = self.transformer.wte(
              idx
          )  # token embeddings of shape (b, t, n_embd)
      else:
          if merge_context:
              assert idx.shape[1] >= 256 + 256 + 1
              t = idx.shape[1] - 256
          else:
              assert (
                  t <= self.config.block_size
              ), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
  
          # forward the GPT model itself
          if merge_context:
              tok_emb = torch.cat(
                  [
                      self.transformer.wte(idx[:, :256])
                      + self.transformer.wte(idx[:, 256 : 256 + 256]),
                      self.transformer.wte(idx[:, 256 + 256 :]),
                  ],
                  dim=1,
              )
          else:
              tok_emb = self.transformer.wte(
                  idx
              )  # token embeddings of shape (b, t, n_embd)
  
      if past_kv is None:
          past_length = 0
          past_kv = tuple([None] * len(self.transformer.h))
      else:
          past_length = past_kv[0][0].size(-2)
  
      if position_ids is None:
          position_ids = torch.arange(
              past_length,
              t + past_length,
              dtype=torch.long,
              device=device,
          )
          position_ids = position_ids.unsqueeze(0)  # shape (1, t)
          assert position_ids.shape == (1, t)
  
      pos_emb = self.transformer.wpe(
          position_ids
      )  # position embeddings of shape (1, t, n_embd)
  
      x = self.transformer.drop(tok_emb + pos_emb)
  
      new_kv = () if use_cache else None
  
      for i, (block, past_layer_kv) in enumerate(
          zip(self.transformer.h, past_kv)
      ):
          x, kv = block(x, past_kv=past_layer_kv, use_cache=use_cache)
  
          if use_cache:
              new_kv = new_kv + (kv,)
  
      x = self.transformer.ln_f(x)
  
      # inference-time mini-optimization: only forward the lm_head on the very last position
      logits = self.lm_head(
          x[:, [-1], :]
      )  # note: using list [-1] to preserve the time dim
  
      return (logits, new_kv)
  
imports: import math
  from dataclasses import dataclass
  import torch
  import torch.nn
  from torch.nn import functional
label_desc: Use image node_id 3 for calling the GPT obj's underlying member method code with example usage: obj.forward(idx, merge_context, past_kv, position_ids, use_cache) and returns: logits,  new_kv