class_docstr: Megatron encoder-decoder base class
class_docstr_tok: ["Megatron","encoder-decoder","base","class"]
code: def configure_optimizers(self):
      if self.with_distributed_adam:
          # Identify params that require grad reductions between
          # pipeline stages
          # See: allreduce_word_and_position_embeddings
          model_parallel_params = []
          if (
              parallel_state.get_pipeline_model_parallel_world_size()
              > 1
              and (parallel_state.is_rank_in_embedding_group())
          ):
              if self.cfg.get(
                  "share_token_embeddings", True
              ) and self.cfg.get(
                  "share_decoder_tokens_head_embeddings", True
              ):
                  model_parallel_params.append(
                      self.enc_dec_model.word_embeddings_weight()
                  )
          if (
              parallel_state.is_rank_in_position_embedding_group()
              and parallel_state.get_pipeline_model_parallel_world_size()
              > 1
              and parallel_state.get_pipeline_model_parallel_split_rank()
              is not None
              and self.cfg.encoder.get("position_embedding_type")
              == "learned_absolute"
              and self.cfg.decoder.get("position_embedding_type")
              == "learned_absolute"
          ):
              if self.cfg.get("share_token_embeddings", True):
                  model_parallel_params.append(
                      self.enc_dec_model.position_embeddings_weight()
                  )
          if (
              parallel_state.get_pipeline_model_parallel_world_size()
              > 2
              and parallel_state.get_pipeline_model_parallel_split_rank()
              is not None
          ):
              if (
                  self.cfg.encoder.get("position_embedding_type")
                  == "relative"
                  and parallel_state.is_rank_in_encoder_relative_position_embedding_group()
                  and parallel_state.get_pipeline_model_parallel_split_rank()
                  > 1
              ):
                  model_parallel_params.append(
                      self.enc_dec_model.encoder_relative_position_embeddings_weight()
                  )
              if (
                  self.cfg.decoder.get("position_embedding_type")
                  == "relative"
                  and parallel_state.is_rank_in_decoder_relative_position_embedding_group()
              ):
                  model_parallel_params.append(
                      self.enc_dec_model.decoder_relative_position_embeddings_weight()
                  )
                  if not self.cfg.decoder.get(
                      "relative_position_bias_self_attention_only", True
                  ):
                      model_parallel_params.append(
                          self.enc_dec_model.decoder_cross_attention_relative_position_embeddings_weight()
                      )
  
          # Disable async grad reductions for params that are
          # synchronized for pipeline parallelism
          for param in model_parallel_params:
              param._disable_greedy_grad_copy = not self.megatron_amp_O2
              param._disable_overlap_grad_sync = True
  
      return super().configure_optimizers()
  
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 3 for calling the MegatronLMEncoderDecoderModel obj's underlying member method code with example usage: obj.configure_optimizers() and returns: super