code: def __init__(
      self,
      args,
      dictionary,
      embed_tokens,
      embed_other_list,
      no_encoder_attn,
      channel_sizes,
  ):
      super().__init__(
          args,
          dictionary,
          embed_tokens,
          no_encoder_attn=no_encoder_attn,
      )
  
      # embed each channel and project if dimensions do not match
      self.embed_other_list = torch.nn.ModuleList(embed_other_list)
      self.proj_other_list = torch.nn.ModuleList()
      dim = embed_tokens.embedding_dim
      for embed_other in embed_other_list:
          other_dim = (
              1 if embed_other is None else embed_other.embedding_dim
          )
          self.proj_other_list.append(
              nn.Linear(other_dim, dim) if other_dim != dim else None
          )
  
      # tranformer output to prediction
      self.channel_sizes = channel_sizes
      self.project_out_dim = Linear(
          embed_tokens.embedding_dim, sum(channel_sizes), bias=False
      )
  
imports: from dataclasses import dataclass, field
  from fairseq.models.fairseq_decoder import FairseqDecoder
  import numpy
  from typing import Optional, Dict, Any, List
  import torch
  from torch import nn
  from fairseq.data.data_utils import compute_mask_indices
  from fairseq.dataclass import ChoiceEnum
  from fairseq.models import FairseqLanguageModel, register_model, register_model_architecture
  from fairseq.tasks.speech_ulm_task import SpeechUnitLanguageModelingTask
  from fairseq.models.transformer import Embedding, TransformerDecoder, Linear
  from fairseq.models.transformer_lm import TransformerLanguageModelConfig
  from torch import Tensor
label_desc: Use image node_id 1 to create a new MultiStreamTransformerDecoder object from inherited base classes: TransformerDecoder with example: obj = MultiStreamTransformerDecoder(args, dictionary, embed_tokens, embed_other_list, no_encoder_attn, channel_sizes)