code: def forward(self, src_tokens, src_lengths):
      encoder_out: Dict[
          str, List[Tensor]
      ] = self.conv_transformer_encoder(
          src_tokens, src_lengths.to(src_tokens.device)
      )
      output = encoder_out["encoder_out"][0]
      encoder_padding_masks = encoder_out["encoder_padding_mask"]
  
      return {
          "encoder_out": [output],
          # This is because that in the original implementation
          # the output didn't consider the last segment as right context.
          "encoder_padding_mask": [
              encoder_padding_masks[0][:, : output.size(0)]
          ]
          if len(encoder_padding_masks) > 0
          else [],
          "encoder_embedding": [],
          "encoder_states": [],
          "src_tokens": [],
          "src_lengths": [],
      }
  
imports: from fairseq import checkpoint_utils
  from fairseq.models import register_model, register_model_architecture
  from fairseq.models.speech_to_text import ConvTransformerModel, convtransformer_espnet, ConvTransformerEncoder
  from fairseq.models.speech_to_text.modules.augmented_memory_attention import augmented_memory, SequenceEncoder, AugmentedMemoryConvTransformerEncoder
  from torch import nn, Tensor
  from typing import Dict, List
  from fairseq.models.speech_to_text.modules.emformer import NoSegAugmentedMemoryTransformerEncoderLayer
label_desc: Use image node_id 2 for calling the ConvTransformerEmformerEncoder obj's underlying member method code with example usage: obj.forward(src_tokens, src_lengths) and returns: dict