docstr: Forward pass.
  
  :param LongTensor[batch,seqlen] input:
      The input IDs
  :param LongTensor[batch,seqlen] positions:
      Positions for input IDs
  :param LongTensor[batch,seqlen] segments:
      If provided, additionally adds ``segments`` as extra embedding features.
docstr_tok: ["Forward","pass",".",":","param","LongTensor","[","batch",",","seqlen","]","input",":","The","input","IDs",":","param","LongTensor","[","batch",",","seqlen","]","positions",":","Positions","for","input","IDs",":","param","LongTensor","[","batch",",","seqlen","]","segments",":","If","provided",",","additionally","adds","``","segments","``","as","extra","embedding","features","."]
code: def forward(
      self,
      input: torch.LongTensor,
      positions: Optional[torch.LongTensor] = None,
      segments: Optional[torch.LongTensor] = None,
  ) -> Tuple[torch.Tensor, torch.BoolTensor]:
      """
      Forward pass.
  
      :param LongTensor[batch,seqlen] input:
          The input IDs
      :param LongTensor[batch,seqlen] positions:
          Positions for input IDs
      :param LongTensor[batch,seqlen] segments:
          If provided, additionally adds ``segments`` as extra embedding features.
      """
      if not self.paralleled:
          self.stack.parallelize()
      mask = input != self.padding_idx
      outputs = self.stack(
          input, attention_mask=mask, output_hidden_states=False
      )
      for k in outputs:
          if torch.is_tensor(outputs[k]):
              outputs[k] = outputs[k].to(input.device)
      return outputs[0], mask
  
imports: import torch
  from typing import Optional, Dict, Any, Tuple
  from transformers import T5ForConditionalGeneration
  from parlai.agents.hugging_face.hugging_face import HF_VERSION
  from parlai.agents.hugging_face.dict import T5DictionaryAgent
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  from parlai.core.torch_agent import Batch, TorchAgent
  from parlai.core.torch_generator_agent import TorchGeneratorAgent, TorchGeneratorModel
  from parlai.utils.fsdp import is_fsdp, delay_halving
label_desc: Use image node_id 2 for calling the ParlaiT5Encoder obj's underlying member method code with example usage: obj.forward(input, positions, segments) and returns: outputs,  mask