class_docstr: A PyTorch RoBERTa implementation
class_docstr_tok: ["A","PyTorch","RoBERTa","implementation"]
code: def forward(
      self, input_tuple: Tuple[torch.Tensor, ...], *args
  ) -> Tuple[torch.Tensor, ...]:
      encoded_layers, pooled_output = (
          self._encoder(input_tuple, args[0])
          if self.use_selfie_encoder or self.skip_token_embed
          else self._encoder(input_tuple)
      )
  
      pad_mask = input_tuple[1]
  
      if self.pooling != PoolingMethod.CLS_TOKEN:
          pooled_output = self._pool_encoded_layers(
              encoded_layers, pad_mask
          )
  
      if self.projection:
          pooled_output = self.projection(pooled_output).tanh()
  
      if pooled_output is not None:
          pooled_output = self.output_dropout(pooled_output)
          if self.normalize_output_rep:
              pooled_output = normalize_embeddings(pooled_output)
  
      output = []
      if self.output_encoded_layers:
          output.append(encoded_layers)
      if self.pooling != PoolingMethod.NO_POOL:
          output.append(pooled_output)
      return tuple(output)
  
imports: import logging
  from typing import Dict, List, Optional, Tuple
  import torch
  from pytext import resources
  from pytext.common.constants import Stage, TORCH_VERSION
  from pytext.config import ConfigBase
  from pytext.data.roberta_tensorizer import RoBERTaTensorizer, RoBERTaTokenLevelTensorizer
  from pytext.data.tensorizers import FloatListTensorizer, LabelTensorizer, NumericLabelTensorizer, Tensorizer
  from pytext.models.bert_classification_models import NewBertModel
  from pytext.models.bert_regression_model import NewBertRegressionModel
  from pytext.models.decoders.mlp_decoder import MLPDecoder
  from pytext.models.model import BaseModel
  from pytext.models.module import create_module, Module
  from pytext.models.output_layers import WordTaggingOutputLayer
  from pytext.models.representations.transformer import MultiheadLinearAttention, PassthroughEncoder, PassthroughTransformer, PostEncoder, QuantizedMultiheadLinearAttention, SELFIETransformer, SentenceEncoder
  from pytext.models.representations.transformer_sentence_encoder_base import PoolingMethod, TransformerSentenceEncoderBase
  from pytext.models.utils import normalize_embeddings
  from pytext.torchscript.module import ScriptPyTextEmbeddingModuleIndex, ScriptPyTextModule, ScriptPyTextModuleWithDense
  from pytext.utils.file_io import PathManager
  from pytext.utils.usage import log_class_usage
  from pytorch.text.fb.nn.modules.multihead_attention import MultiheadSelfAttention
  from pytorch.text.fb.nn.modules.transformer import DEFAULT_MAX_SEQUENCE_LENGTH, Transformer, TransformerLayer
  from torch import nn
  from torch.serialization import default_restore_location
  from .r3f_models import R3FConfigOptions, R3FPyTextMixin
label_desc: Use image node_id 4 for calling the RoBERTaEncoder obj's underlying member method code with example usage: obj.forward(input_tuple) and returns: tuple