class_docstr: Cross-Attention Transformer Decoder Layer block as described
  in the paper: https://arxiv.org/pdf/2203.16502.pdf
  
  Composed of a Multi-head Self Attention block followed by a
  Multi-head Cross-Attention block which attends to the self-attention
  outputs of the other channels. The weights of the attention blocks
  in all channels are shared.
  
  Args:
      args (argparse.Namespace): parsed command-line arguments
      no_encoder_attn (bool, optional): whether to attend to encoder outputs
          (default: False).
class_docstr_tok: ["Cross-Attention","Transformer","Decoder","Layer","block","as","described","in","the","paper",":","https",":","\/\/arxiv.org\/pdf\/2203.16502.pdf","Composed","of","a","Multi-head","Self","Attention","block","followed","by","a","Multi-head","Cross-Attention","block","which","attends","to","the","self-attention","outputs","of","the","other","channels",".","The","weights","of","the","attention","blocks","in","all","channels","are","shared",".","Args",":","args","(","argparse.Namespace",")",":","parsed","command-line","arguments","no_encoder_attn","(","bool",",","optional",")",":","whether","to","attend","to","encoder","outputs","(","default",":","False",")","."]
code: def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size):
      return quant_noise(
          nn.Linear(input_dim, output_dim), q_noise, qn_block_size
      )
  
imports: from typing import Dict, List, Tuple, Optional
  import torch
  import torch.nn
  from fairseq import utils
  from fairseq.modules import LayerNorm, MultiheadAttention
  from fairseq.modules.fairseq_dropout import FairseqDropout
  from fairseq.modules.quant_noise import quant_noise
  from torch import Tensor
label_desc: Use image node_id 3 for calling the CrossChannelTransformerDecoderLayer obj's underlying member method code with example usage: obj.build_fc2(input_dim, output_dim, q_noise, qn_block_size) and returns: quant_noise