code: def prepare_for_training(self):
      # 1/10/23: For parity with how the LLM model type sets up adapters and quantization, LLM encoders should call
      # `prepare_for_training` at training time rather than at initialization. This loop searches for input features
      # using the LLM encoder and calls `prepare_for_training` on those encoders only. No other changes should be
      # made to the ECD model itself or any other encoders.
      for feature in self.config_obj.input_features:
          encoder_type = feature.encoder.type
          if encoder_type == MODEL_LLM:
              feature_name = feature.name
              encoder = self.input_features.get(feature_name)
              encoder.prepare_for_training()
  
imports: import logging
  import os
  from typing import Dict, Tuple, Union
  import numpy
  import torch
  from ludwig.accounting.used_tokens import get_used_tokens_for_ecd
  from ludwig.combiners.combiners import create_combiner
  from ludwig.constants import MODEL_ECD, MODEL_LLM, USED_TOKENS
  from ludwig.globals import MODEL_WEIGHTS_FILE_NAME
  from ludwig.models.base import BaseModel
  from ludwig.schema.model_types.ecd import ECDModelConfig
  from ludwig.utils import output_feature_utils
  from ludwig.utils.augmentation_utils import AugmentationPipelines
  from ludwig.utils.data_utils import clear_data_cache
  from ludwig.utils.fs_utils import open_file
  from ludwig.utils.state_dict_backward_compatibility import update_state_dict
  from ludwig.utils.torch_utils import get_torch_device
label_desc: Use image node_id 3 for calling the ECD obj's underlying member method code with example usage: obj.prepare_for_training() without return types