class_docstr: Builder type to hide complex configuration of PTL Trainers for Megatron LLM models.
  Can be extended to change behavior for a specific model.
docstr: Returns:
      plugins: list of plugins passed to Trainer.plugins including precision plugins.
docstr_tok: ["Returns",":","plugins",":","list","of","plugins","passed","to","Trainer.plugins","including","precision","plugins","."]
code: def _plugins(self) -> list:
      """
      Returns:
          plugins: list of plugins passed to Trainer.plugins including precision plugins.
      """
      megatron_amp_O2 = self.cfg.model.get("megatron_amp_O2", False)
      with_distributed_adam = (
          self.cfg.model.optim.get("name") == "distributed_fused_adam"
          if self.cfg.model.get("optim")
          else False
      )
  
      plugins = []
      if self.cfg.trainer.precision in [
          16,
          "16",
          "bf16",
          "16-mixed",
          "bf16-mixed",
      ]:
          scaler = None
          if self.cfg.trainer.precision in [16, "16", "16-mixed"]:
              scaler = self._grad_scaler()
              plugin_precision = "16-mixed"
          else:
              plugin_precision = "bf16-mixed"
  
          if megatron_amp_O2 and not with_distributed_adam:
              plugins.append(
                  MegatronHalfPrecisionPlugin(
                      precision=plugin_precision,
                      device="cuda",
                      scaler=scaler,
                  )
              )
          else:
              plugins.append(
                  PipelineMixedPrecisionPlugin(
                      precision=plugin_precision,
                      device="cuda",
                      scaler=scaler,
                  )
              )
  
      if self.cfg.get("cluster_type", None) == "BCP":
          plugins.append(TorchElasticEnvironment())
  
      return plugins
  
imports: import sys
  from typing import Union
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from pytorch_lightning.callbacks import ModelSummary
  from pytorch_lightning.plugins.environments import TorchElasticEnvironment
  from nemo.collections.nlp.parts.nlp_overrides import CustomProgressBar, GradScaler, MegatronHalfPrecisionPlugin, NLPDDPStrategy, NLPDDPStrategyNotebook, NLPFSDPStrategy, PipelineMixedPrecisionPlugin
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the MegatronTrainerBuilder obj's underlying member method code with example usage: obj._plugins() and returns: plugins