code: def instantiate_text_encoder(self, cfg):
      model = DreamBooth.from_config_dict(cfg)
      if self.train_text_encoder:
          self.text_encoder = model.train()
          for param in self.text_encoder.parameters():
              param.requires_grad = True
      else:
          self.text_encoder = model.eval()
          self.text_encoder.train = disabled_train
          for param in self.text_encoder.parameters():
              param.requires_grad = False
  
imports: from functools import partial
  from typing import Any, Optional
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from torch._inductor import config
  from nemo.collections.multimodal.data.dreambooth.dreambooth_dataset import DreamBoothDataset
  from nemo.collections.multimodal.modules.stable_diffusion.distributions.distributions import DiagonalGaussianDistribution
  from nemo.collections.multimodal.parts.utils import randn_like
  from nemo.collections.nlp.data.language_modeling.megatron.data_samplers import MegatronPretrainingRandomSampler
  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 4 for calling the DreamBooth obj's underlying member method code with example usage: obj.instantiate_text_encoder(cfg) without return types