docstr: Forward pass.
  
  :param LongTensor[batch,seqlen] input:
      The decoder inputs (partial or full decoded token IDs).
  :param encoder_state:
      Output from the encoder module forward pass.
  :param incr_state:
      The incremental state: a dictionary whose keys index the layers and whose
      values contain the incremental state for each layer.
docstr_tok: ["Forward","pass",".",":","param","LongTensor","[","batch",",","seqlen","]","input",":","The","decoder","inputs","(","partial","or","full","decoded","token","IDs",")",".",":","param","encoder_state",":","Output","from","the","encoder","module","forward","pass",".",":","param","incr_state",":","The","incremental","state",":","a","dictionary","whose","keys","index","the","layers","and","whose","values","contain","the","incremental","state","for","each","layer","."]
code: def forward(
      self,
      input: torch.LongTensor,
      encoder_state: Tuple[Any],
      incr_state=None,
  ):
      """
      Forward pass.
  
      :param LongTensor[batch,seqlen] input:
          The decoder inputs (partial or full decoded token IDs).
      :param encoder_state:
          Output from the encoder module forward pass.
      :param incr_state:
          The incremental state: a dictionary whose keys index the layers and whose
          values contain the incremental state for each layer.
      """
      if not self.paralleled:
          self.stack.parallelize()
      encoder_output, encoder_mask = encoder_state
  
      mask = input != self.padding_idx
      mask[:, 0] = True  # first token is pad
  
      outputs = self.stack(
          input_ids=input,
          attention_mask=mask,
          encoder_hidden_states=encoder_output.to(input.device),
          encoder_attention_mask=encoder_mask.to(input.device),
      )
      return outputs[0].to(input.device), incr_state
  
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 ParlaiT5Decoder obj's underlying member method code with example usage: obj.forward(input, encoder_state, incr_state) and returns: outputs,  incr_state