code: def forward(self, hidden_states):
      # [s, b, 4 * h/p]
      intermediate_parallel, bias_parallel = self.linear_fc1(
          hidden_states
      )
  
      if self.config.bias_gelu_fusion:
          assert self.config.add_bias_linear is True
          assert self.activation_func == F.gelu
          intermediate_parallel = bias_gelu_impl(
              intermediate_parallel, bias_parallel
          )
      else:
          if bias_parallel is not None:
              intermediate_parallel = (
                  intermediate_parallel + bias_parallel
              )
          intermediate_parallel = self.activation_func(
              intermediate_parallel
          )
  
      infused_adapter = self.get_adapter_module(AdapterName.MLP_INFUSED)
      if infused_adapter:
          intermediate_parallel = infused_adapter(intermediate_parallel)
  
      # [s, b, h]
      output, output_bias = self.linear_fc2(intermediate_parallel)
      return output, output_bias
  
imports: import torch
  import torch.nn.functional
  from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add
  from megatron.core.fusions.fused_bias_gelu import bias_gelu_impl
  from megatron.core.models.common.embeddings.language_model_embedding import LanguageModelEmbedding
  from megatron.core.transformer.attention import SelfAttention
  from megatron.core.transformer.custom_layers.transformer_engine import TEColumnParallelLinear, TELayerNormColumnParallelLinear
  from megatron.core.transformer.mlp import MLP
  from megatron.core.transformer.transformer_layer import TransformerLayer
  from megatron.core.utils import make_viewless_tensor
  from nemo.collections.nlp.modules.common.megatron.adapters.parallel_adapters import AdapterName, InfusedAdapterConfig, LoraKQVAdapterConfig, MLPInfusedAdapterConfig, ParallelLinearAdapterConfig, PromptEncoderAdapterConfig
  from nemo.core import adapter_mixins
label_desc: Use image node_id 2 for calling the MCoreMLPMixin obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: output,  output_bias