class_docstr: Machine translation model which supports bottleneck architecture,
  NLL, VAE, and MIM loss.
  
  Supported losses:
    1) nll - Conditional cross entropy (the usual NMT loss)
    2) mim - MIM learning framework. A latent variable model with good
                    reconstruction and compressed latent representation.
                    https://arxiv.org/pdf/2003.02645.pdf
    3) vae - VAE learning framework. A latent variable model which learns
                    good probability estimation over observations and
                    a regularized latent representation.
                    https://arxiv.org/pdf/1312.6114.pdf
class_docstr_tok: ["Machine","translation","model","which","supports","bottleneck","architecture",",","NLL",",","VAE",",","and","MIM","loss",".","Supported","losses",":","1",")","nll","-","Conditional","cross","entropy","(","the","usual","NMT","loss",")","2",")","mim","-","MIM","learning","framework",".","A","latent","variable","model","with","good","reconstruction","and","compressed","latent","representation",".","https",":","\/\/arxiv.org\/pdf\/2003.02645.pdf","3",")","vae","-","VAE","learning","framework",".","A","latent","variable","model","which","learns","good","probability","estimation","over","observations","and","a","regularized","latent","representation",".","https",":","\/\/arxiv.org\/pdf\/1312.6114.pdf"]
docstr: Validate encoder and decoder hidden sizes, and enforce same size.
  We support here encoder/decoder with different hidden_size, so do nothing.
docstr_tok: ["Validate","encoder","and","decoder","hidden","sizes",",","and","enforce","same","size",".","We","support","here","encoder\/decoder","with","different","hidden_size",",","so","do","nothing","."]
code: def _validate_encoder_decoder_hidden_size(self):
      """
      Validate encoder and decoder hidden sizes, and enforce same size.
      We support here encoder/decoder with different hidden_size, so do nothing.
      """
      pass
  
imports: from typing import Dict, Optional
  import numpy
  import torch
  from pytorch_lightning import Trainer
  from nemo.collections.common.losses import NLLLoss
  from nemo.collections.nlp.models.machine_translation.mt_enc_dec_config import MTBottleneckModelConfig
  from nemo.collections.nlp.models.machine_translation.mt_enc_dec_model import MTEncDecModel
  from nemo.core.classes.common import typecheck
  from nemo.utils import timers
label_desc: Use image node_id 2 for calling the MTBottleneckModel obj's underlying member method code with example usage: obj._validate_encoder_decoder_hidden_size() without return types