class_docstr: For brevity this subclasses TransformerEncoder, but you could write your own
  nn.Module from scratch as long as the __init__ and forward signatures match
  TransformerEncoder.
class_docstr_tok: ["For","brevity","this","subclasses","TransformerEncoder",",","but","you","could","write","your","own","nn.Module","from","scratch","as","long","as","the","__init__","and","forward","signatures","match","TransformerEncoder","."]
code: def forward(
      self,
      input: torch.LongTensor,
      positions: Optional[torch.LongTensor] = None,
      segments: Optional[torch.LongTensor] = None,
  ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.BoolTensor]]:
      logging.info("Custom encoder called!")
      # Comment out the following line and write your custom `forward` instead.
      return super().forward(input, positions, segments)
  
imports: from __future__ import annotations
  import torch
  from enum import Enum
  from typing import Dict, Optional, Tuple, Union
  from parlai.agents.transformer.modules import TransformerFFN, MultiHeadAttention, TransformerDecoder, TransformerDecoderLayer, TransformerEncoder, TransformerEncoderLayer, TransformerGeneratorModel
  from parlai.agents.transformer.transformer import TransformerGeneratorAgent
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  import parlai.utils.logging
label_desc: Use image node_id 1 for calling the MyCustomEncoder obj's underlying member method code with example usage: obj.forward(input, positions, segments) and returns: super