class_docstr: Megatron encoder-decoder base class
class_docstr_tok: ["Megatron","encoder-decoder","base","class"]
code: def get_forward_output_and_loss_func(self):
      def fwd_output_and_loss_func(dataloader_iter, model):
          batch = next(dataloader_iter)
          # convert to list if not already converted.
          if isinstance(batch, dict):
              # convert to list if not already converted.
              batch = self._process_batch(batch)
          batch = [
              x.cuda(non_blocking=True) if torch.is_tensor(x) else x
              for x in batch
          ]
          (
              encoder_input_ids,
              decoder_input_ids,
              loss_mask,
              lm_labels,
              encoder_attn_mask,
              decoder_attn_mask,
              batch_data,
          ) = batch
  
          output = model(
              encoder_input_ids,  # enc_input_ids
              encoder_attn_mask,  # enc_attn_mask
              decoder_input_ids,  # dec_input_ids
              decoder_attn_mask,  # dec_attn_mask
              None,  # token_type_ids
              lm_labels,  # labels
              batch_data,  # batch_data
          )
  
          def loss_func(output_tensor):
              if isinstance(output_tensor, dict):
                  # handle loss of hidden transformations
                  loss_dict = output_tensor
                  output_tensor = loss_dict.pop("output")
                  # compute reconstruction (tokens) only loss from per-token reconstruction loss
                  tokens_loss = self.loss_func(loss_mask, output_tensor)
                  loss_dict["tokens_loss"] = tokens_loss
                  tokens_loss_weight = loss_dict.get(
                      "tokens_loss_weight", 1.0
                  )
                  # compute total loss
                  loss = loss_dict["loss"] = (
                      loss_dict["hiddens_loss"]
                      + tokens_loss_weight * tokens_loss
                  )
                  # average losses across data parallel group
                  loss_dict = {
                      k: average_losses_across_data_parallel_group(
                          [v.mean()]
                      )
                      for k, v in loss_dict.items()
                  }
              else:
                  # compute reconstruction (tokens) only loss from per-token reconstruction loss
                  loss = self.loss_func(loss_mask, output_tensor)
                  # average losses across data parallel group
                  reduced_loss = (
                      average_losses_across_data_parallel_group([loss])
                  )
                  loss_dict = {"loss": reduced_loss}
  
              return loss, loss_dict
  
          return output, loss_func
  
      return fwd_output_and_loss_func
  
imports: import copy
  import functools
  import inspect
  from typing import Any, Dict, List, Optional
  import torch
  from omegaconf import OmegaConf, open_dict
  from omegaconf.dictconfig import DictConfig
  from pytorch_lightning.accelerators import CPUAccelerator
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.nlp.data.language_modeling.megatron.data_samplers import MegatronPretrainingRandomSampler, MegatronPretrainingSampler
  from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel
  from nemo.collections.nlp.modules.common.megatron.build_model import build_model
  from nemo.collections.nlp.modules.common.megatron.module import Float16Module
  from nemo.collections.nlp.modules.common.megatron.token_level_encoder_decoder import MegatronTokenLevelEncoderDecoderModule
  from nemo.collections.nlp.modules.common.megatron.utils import ApexGuardDefaults, average_losses_across_data_parallel_group, get_params_for_weight_decay_optimization
  from nemo.collections.nlp.modules.common.text_generation_utils import compute_beam_search_len_penalty, get_sampling_token_fn
  from nemo.collections.nlp.parts.utils_funcs import get_last_rank
  from nemo.utils import AppState, logging
label_desc: Use image node_id 18 for calling the MegatronLMEncoderDecoderModel obj's underlying member method code with example usage: obj.get_forward_output_and_loss_func() and returns: fwd_output_and_loss_func, output,  loss_func, loss,  loss_dict