code: def forward(self, input_ids, position_ids=None):
      hidden_states = self.embeddings(
          input_ids, position_ids=position_ids
      )
      # TD [2022-07-30]: Force residual in fp32, seems to make fp16 training more stable
      if not self.fused_dropout_add_ln:
          residual = self.emb_drop(hidden_states).float()
          hidden_states = self.ln_0(
              residual.to(dtype=self.ln_0.weight.dtype)
          )
      else:
          hidden_states, residual = dropout_add_layer_norm(
              hidden_states,
              None,
              self.ln_0.weight,
              self.ln_0.bias,
              self.emb_drop.p if self.training else 0.0,
              self.ln_0.eps,
              prenorm=True,
              residual_in_fp32=True,
          )
      for layer in self.layers:
          hidden_states, residual = layer(hidden_states, residual)
      return hidden_states
  
imports: import math
  from functools import partial
  from collections import namedtuple
  from collections.abc import Sequence
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers.models.gpt2.configuration_gpt2 import GPT2Config
  from src.models.modules.mha import MHA
  from src.models.modules.mlp import Mlp, FusedDenseGeluDense
  from src.models.modules.block import Block
  from src.models.modules.embedding import GPT2Embeddings
label_desc: Use image node_id 2 for calling the GPT2Model obj's underlying member method code with example usage: obj.forward(input_ids, position_ids) and returns: hidden_states