code: def forward(self, x, valid_length):
      atten_mask = gen_self_attn_mask(
          x,
          valid_length,
          layout=self._layout,
          dtype=self._dtype,
          attn_type="full",
      )
      all_encodings_outputs = [x]
      additional_outputs = []
      for layer_idx in range(self.num_layers):
          layer = self.all_layers[layer_idx]
          x, attention_weights = layer(x, atten_mask)
          if self._output_all_encodings:
              all_encodings_outputs.append(x)
          if self._output_attention:
              additional_outputs.append(attention_weights)
      # sequence_mask is not necessary here because masking could be performed in downstream tasks
      if self._output_all_encodings:
          return all_encodings_outputs, additional_outputs
      else:
          return x, additional_outputs
  
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 RobertaEncoder obj's underlying member method code with example usage: obj.forward(x, valid_length) and returns: all_encodings_outputs,  additional_outputs, x,  additional_outputs