code: def _forward(self, hidden_states, attention_mask) -> Tensor:
      if not self.apply_post_layer_norm:
          residual = hidden_states
      hidden_states = self.norm1(hidden_states)
      if self.apply_post_layer_norm:
          residual = hidden_states
      attention_output = self.attention(hidden_states, attention_mask)
      hidden_states = residual + attention_output
  
      if not self.apply_post_layer_norm:
          residual = hidden_states
      hidden_states = self.norm2(hidden_states)
      if self.apply_post_layer_norm:
          residual = hidden_states
      feed_forward_hidden_states = self.mlp(hidden_states)
      hidden_states = residual + feed_forward_hidden_states
  
      output = (hidden_states, attention_mask)
      return output
  
imports: import math
  import torch
  from torch import Tensor
  from torch import nn
  from colossalai import kernel
  from colossalai import nn
  from colossalai.kernel.cuda_native.scaled_softmax import AttnMaskType
  from colossalai.legacy.core import global_context
  from colossalai.legacy.nn.layer import Linear1D_Col, Linear1D_Row
  from colossalai.legacy.nn.layer.base_layer import ParallelLayer
  from colossalai.legacy.nn.layer.utils import ACT2FN, divide
  from colossalai.legacy.utils.activation_checkpoint import checkpoint
  from colossalai.utils import checkpoint
label_desc: Use image node_id 2 for calling the GenericGPTTransformerLayer1D obj's underlying member method code with example usage: obj._forward(hidden_states, attention_mask) and returns: output