code: def forward(self, input_ids, position_ids):
      encoder_input = super().forward(input_ids, position_ids)
  
      if self.is_adapter_available():
          _sq, _bs, _hs = encoder_input.size()
          ptuning_adapter = self.get_adapter_module(
              AdapterName.PTUNING_ADAPTER
          )
          v = ptuning_adapter.virtual_tokens
          if (
              ptuning_adapter and _sq >= v
          ):  # The sequence should be longer the v to insert virtual embeddings.
              virtual_embeddings = ptuning_adapter(_bs)
              encoder_input = encoder_input[
                  v:, :, :
              ]  # the first v tokens are pads so that they can be swapped out with virtual embeddings.
              encoder_input = torch.concat(
                  [virtual_embeddings, encoder_input], dim=0
              )
      return encoder_input
  
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 MCoreGPTEmbeddingMixin obj's underlying member method code with example usage: obj.forward(input_ids, position_ids) and returns: encoder_input