code: def forward(
      self, hidden_states=None, input_ids=None, attention_mask=None
  ):
      if self.embedding is not None:
          hidden_states = self.embedding(input_ids=input_ids)
      batch_size = hidden_states.shape[0]
      attention_mask = attention_mask.view(batch_size, -1)
      attention_mask = attention_mask[:, None, None, :]
      attention_mask = attention_mask.to(
          dtype=hidden_states.dtype
      )  # fp16 compatibility
      attention_mask = (1.0 - attention_mask) * -10000.0
      for block in self.blocks:
          hidden_states, attention_mask = block(
              hidden_states, attention_mask
          )
      if self.norm is not None:
          hidden_states = self.head(self.norm(hidden_states))
      return hidden_states
  
imports: import inspect
  import titans.model.gpt.gpt
  import torch
  import torch.nn
  from colossalai import kernel
  from colossalai import nn
  from colossalai.legacy.context.parallel_mode import ParallelMode
  from colossalai.legacy.core import global_context
  from colossalai.legacy.nn.layer.wrapper import PipelineSharedModuleWrapper
  from colossalai.legacy.pipeline.utils import partition_uniform
  from colossalai.logging import get_dist_logger
  from .embed import HiddenParallelEmbedding, HiddenParallelGPTLMHead1D, VocabParallelEmbedding, VocabParallelGPTLMHead1D
  from .gpt1d import FusedGPTTransformerLayer1D, GPTTransformerLayer1D
label_desc: Use image node_id 2 for calling the GenericPipelineGPT obj's underlying member method code with example usage: obj.forward(hidden_states, input_ids, attention_mask) and returns: hidden_states