class_docstr: Megatron base class. All NeMo Megatron models inherit from this class.
  
  - Initialize the model parallel world for nemo.
  - Turn on all of the nvidia optimizations.
  - If `cfg.tokenizer` is available, it loads the tokenizer and pad the vocab to the
    correct size for tensor model parallelism.
  - If using distributed optimizer, configure to be compatible
    with O2 level optimizations and/or model parallelism.
  - Perform gradient clipping: `grad_clip_pl_default` triggers
    the PyTorch Lightning default implementation, `with_distributed_adam` triggers
    the distributed optimizer's implementation, `megatron_amp_O2` triggers gradient clipping on the main grads,
    and otherwise gradient clipping is performed on the model grads.
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code: def configure_optimizers(self):
      self.setup_optimization()
  
      # Wrap the baseline optimizer with the optimizer class with master parameters
      if (
          self.megatron_amp_O2
          and not self.with_distributed_adam
          and self._optimizer is not None
      ):
          if self.torch_dtype == torch.bfloat16:
              fp32_grad_accum = True
              contiguous_grad_bucket = True
          elif self.torch_dtype == torch.float16:
              fp32_grad_accum = False
              # TODO: contiguous grad bucket for fp16 is also planned to be supported
              contiguous_grad_bucket = False
              raise ValueError(
                  "fp16 training is not yet supported with O2. Please set megatron_amp_O2 to False in the model config."
              )
  
          # if using tensor parallel only, we automatically use async grad all-reduce
          # if using pipeline parallel or sequence parallel or gradient accumulation fusion, then we disable it
          if (
              self.cfg.get("pipeline_model_parallel_size", 1) == 1
              and not (
                  self.cfg.get("sequence_parallel", False)
                  or self.cfg.get("gradient_accumulation_fusion", False)
              )
              and self.cfg.get("async_grad_allreduce", True)
          ):
              async_grad_allreduce = True
          else:
              async_grad_allreduce = False
  
          if async_grad_allreduce:
              # we need this to be configurable until make_nccl_premul_sum is in public PyTorch.
              # currently cannot be imported in PyTorch 1.12.0
              grad_div_ar_fusion = self.cfg.get(
                  "grad_div_ar_fusion", False
              )
          else:
              grad_div_ar_fusion = False
  
          self._optimizer = MainParamsOptimizerWrapper(
              self._optimizer,
              fp32_grad_accum=fp32_grad_accum,
              contiguous_grad_bucket=contiguous_grad_bucket,
              async_grad_allreduce=async_grad_allreduce,
              grad_div_ar_fusion=grad_div_ar_fusion,
              grad_allreduce_chunk_size_mb=self.cfg.get(
                  "grad_allreduce_chunk_size_mb", 125
              ),
          )
  
          assert (
              self._trainer.max_steps is not None
          ), "'max_steps' is missing in trainer config."
          if hasattr(self._cfg.optim, "sched"):
              sched_config = self._cfg.optim.sched
              sched_config["max_steps"] = self._trainer.max_steps
              self._scheduler = prepare_lr_scheduler(
                  optimizer=self._optimizer,
                  scheduler_config=sched_config,
                  train_dataloader=self._train_dl,
              )
  
      # Configure distributed optimizer
      if self.with_distributed_adam:
          # Initialize param buckets if explicitly provided
          if getattr(self, "distributed_adam_buckets", None):
              for bucket in self.distributed_adam_buckets:
                  self._optimizer.init_params_bucket(bucket)
              self._optimizer.init_params_bucket(self.parameters())
          if hasattr(self, "distributed_adam_buckets"):
              del self.distributed_adam_buckets
  
          # Make sure all params are initialized so main grads are
          # available
          # Note: Consolidate grads without overlap
          overlap_params = []
          no_overlap_params = []
          for p in self.parameters():
              if getattr(p, "_disable_overlap_grad_sync", False):
                  no_overlap_params.append(p)
              else:
                  overlap_params.append(p)
          self._optimizer.init_params(reversed(overlap_params))
          self._optimizer.init_params(reversed(no_overlap_params))
  
          # Initialize contiguous parameter buffer
          if self._optimizer.contiguous_param_buffer:
              self._optimizer.init_param_buffer()
  
      if self._scheduler is None:
          return self._optimizer
      else:
          return [self._optimizer], [self._scheduler]
  
imports: import gc
  import itertools
  import os
  import re
  from dataclasses import fields
  from typing import Any, Dict, Optional, Union
  import omegaconf
  import torch
  from omegaconf import OmegaConf, open_dict
  from omegaconf.dictconfig import DictConfig
  from pytorch_lightning.plugins.precision import MixedPrecisionPlugin
  from pytorch_lightning.trainer.connectors.logger_connector.fx_validator import _FxValidator
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.nlp.models.nlp_model import NLPModel
  from nemo.collections.nlp.modules.common.megatron.attention import HAVE_FLASH_ATTENTION
  from nemo.collections.nlp.modules.common.megatron.clip_grads import clip_grad_norm_distributed_optimizer, clip_grad_norm_fp32
  from nemo.collections.nlp.modules.common.megatron.megatron_init import initialize_model_parallel_for_nemo
  from nemo.collections.nlp.modules.common.megatron.module import Float16Module
  from nemo.collections.nlp.modules.common.megatron.utils import ApexGuardDefaults
  from nemo.collections.nlp.modules.common.tokenizer_utils import get_nmt_tokenizer
  from nemo.collections.nlp.parts import utils_funcs
  from nemo.collections.nlp.parts.nlp_overrides import NEMO_MEGATRON_MODEL_PARALLEL_APPSTATE_OVERRIDE, GradScaler
  from nemo.collections.nlp.parts.utils_funcs import activation_to_func
  from nemo.core.optim import MainParamsOptimizerWrapper, prepare_lr_scheduler
  from nemo.utils import AppState, logging, str_to_dtype
  from nemo.utils.get_rank import is_global_rank_zero
label_desc: Use image node_id 23 for calling the MegatronBaseModel obj's underlying member method code with example usage: obj.configure_optimizers() and returns: self, list,  list