code: def forward(self, tokens, valid_length):
      embedding = self.get_initial_embedding(tokens)
      if self._layout != self._compute_layout:
          contextual_embeddings, additional_outputs = self.encoder(
              np.swapaxes(embedding, 0, 1), valid_length
          )
          contextual_embeddings = np.swapaxes(
              contextual_embeddings, 0, 1
          )
      else:
          contextual_embeddings, additional_outputs = self.encoder(
              embedding, valid_length
          )
      if self.use_pooler:
          if isinstance(contextual_embeddings, list):
              pooled_out = self.apply_pooling(contextual_embeddings[-1])
          else:
              pooled_out = self.apply_pooling(contextual_embeddings)
          return contextual_embeddings, pooled_out
      else:
          return contextual_embeddings
  
imports: import os
  from typing import Tuple
  import mxnet
  from mxnet import use_np, np, npx
  from mxnet.gluon import HybridBlock, nn
  from .base import BACKBONE_REGISTRY
  from ..op import select_vectors_by_position
  from ..base import get_model_zoo_home_dir, get_repo_model_zoo_url, get_model_zoo_checksum_dir
  from ..layers import PositionalEmbedding, get_activation
  from ..utils.misc import download, load_checksum_stats
  from ..utils.registry import Registry
  from .transformer import TransformerEncoderLayer
  from ..initializer import TruncNorm
  from ..utils.config import CfgNode
  from ..attention_cell import gen_self_attn_mask
  from ..data.tokenizers import HuggingFaceByteBPETokenizer
label_desc: Use image node_id 3 for calling the RobertaModel obj's underlying member method code with example usage: obj.forward(tokens, valid_length) and returns: contextual_embeddings,  pooled_out, contextual_embeddings