code: def __init__(self, cfg: DictConfig, trainer: Trainer):
      if not HAVE_APEX:
          raise ImportError(
              "Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt."
          )
  
      with open_dict(cfg):
          cfg.hidden_size = cfg.unet.embed_dim
      # this prevents base constructor from initializing tokenizer
      self.tokenizer = None
      super().__init__(cfg, trainer=trainer)
  
      self._validate_trainer()
      # megatron_amp_O2 is not yet supported in diffusion models
      self.megatron_amp_O2 = cfg.get("megatron_amp_O2", False)
  
      self.model = self.model_provider_func()
  
      if self.trainer.precision in ["bf16", "bf16-mixed"]:
          self.autocast_dtype = torch.bfloat16
      elif self.trainer.precision in [32, "32", "32-true"]:
          self.autocast_dtype = torch.float
      elif self.trainer.precision in [16, "16", "16-mixed"]:
          self.autocast_dtype = torch.half
      else:
          raise ValueError(
              'precision must be in ["32-true", "16-mixed", "bf16-mixed"]'
          )
  
      self.online_encoding = cfg.conditioning.get(
          "online_encoding", False
      )
      self.text_encoder_path = cfg.conditioning.get(
          "encoder_path", None
      )
  
imports: import itertools
  from datetime import datetime
  from functools import partial
  from typing import Any
  import torch
  from omegaconf import DictConfig, open_dict
  from pytorch_lightning import Trainer
  from nemo.collections.multimodal.data.imagen.imagen_dataset import build_train_valid_datasets
  from nemo.collections.multimodal.models.text_to_image.imagen.precond import ContinousDDPMPrecond, EDMPrecond
  from nemo.collections.multimodal.modules.imagen.diffusionmodules.nets import EfficientUNetModel, UNetModel
  from nemo.collections.multimodal.modules.imagen.encoder.t5encoder import T5Encoder
  from nemo.collections.multimodal.modules.imagen.sampler.sampler import DDPMSampler, EDMSampler
  from nemo.collections.multimodal.parts.imagen.utils import random_dropout
  from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel
  from nemo.collections.nlp.modules.common.megatron.module import Float16Module
  from nemo.collections.nlp.parts.utils_funcs import get_last_rank
  from nemo.core.classes.common import Serialization
  from nemo.utils import logging
label_desc: Use image node_id 1 to create a new MegatronImagen object from inherited base classes: MegatronBaseModel with example: obj = MegatronImagen(cfg, trainer)