code: def __init__(self, cfg: DictConfig, trainer: Trainer):
      super(MegatronContentFilteringModel, self).__init__(cfg, trainer)
  
      self.model = build_model(
          model_provider_func=self.model_provider_func,
          wrap_with_ddp=False,
          on_cpu=isinstance(self.trainer.accelerator, CPUAccelerator),
          virtual_pipeline_model_parallel_size=None,
      )
      self.model = self.model[0]
  
      self.megatron_amp_O2 = cfg.get("megatron_amp_O2", False)
      if self.megatron_amp_O2:
          if isinstance(self.model, list):
              self.model = [
                  Float16Module(
                      config=self.model_parallel_config,
                      module=x,
                      precision=cfg.precision,
                  )
                  for x in self.model
              ]
          else:
              self.model = Float16Module(
                  config=self.model_parallel_config,
                  module=self.model,
                  precision=cfg.precision,
              )
  
      self.autocast_dtype = torch_dtype_from_precision(
          self.trainer.precision
      )
      self.enable_autocast = (not self.megatron_amp_O2) and (
          self.autocast_dtype in [torch.float16, torch.bfloat16]
      )
  
      self.init_consumed_samples = 0
      self.mlp_factor = 1.0
      self.emb_factor = 1.0
  
      self.validation_metrics = None
  
imports: import functools
  import itertools
  from typing import List, Optional, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from apex.transformer.pipeline_parallel.utils import get_num_microbatches
  from megatron.core import parallel_state
  from megatron.core.pipeline_parallel.schedules import get_forward_backward_func
  from omegaconf.dictconfig import DictConfig
  from pytorch_lightning.accelerators import CPUAccelerator
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.multimodal.data.clip.clip_dataset import tokenize
  from nemo.collections.multimodal.data.nsfw.nsfw_dataset import build_dataset
  from nemo.collections.multimodal.models.vision_language_foundation.clip.megatron_clip_models import CLIPTextTransformer, CLIPVisionTransformer
  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, MegatronModule
  from nemo.collections.nlp.parts.utils_funcs import get_last_rank, torch_dtype_from_precision
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 1 to create a new MegatronContentFilteringModel object from inherited base classes: MegatronBaseModel with example: obj = MegatronContentFilteringModel(cfg, trainer)