code: def _init_feature_encoders(self, attr):
      feat_encoders = []
      feat_encoders_list_config = self.config[
          attr + "_feature_encodings"
      ]
      feature_dim = self.config[attr + "_feature_dim"]
      setattr(self, attr + "_feature_dim", feature_dim)
  
      for feat_encoder in feat_encoders_list_config:
          feat_encoder_config = copy.deepcopy(feat_encoder)
          with omegaconf.open_dict(feat_encoder_config):
              feat_encoder_config.params.model_data_dir = (
                  self.config.model_data_dir
              )
              feat_encoder_config.params.in_dim = feature_dim
          feat_model = build_image_encoder(
              feat_encoder_config, direct_features=True
          )
          feat_encoders.append(feat_model)
          setattr(self, attr + "_feature_dim", feat_model.out_dim)
  
      setattr(
          self, attr + "_feature_encoders", nn.ModuleList(feat_encoders)
      )
  
imports: import copy
  import omegaconf
  import torch
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.modules.embeddings import ImageFeatureEmbedding, MultiHeadImageFeatureEmbedding, PreExtractedEmbedding, TextEmbedding
  from mmf.modules.layers import ClassifierLayer, ModalCombineLayer
  from mmf.utils.build import build_image_encoder
  from torch import nn
label_desc: Use image node_id 8 for calling the Pythia obj's underlying member method code with example usage: obj._init_feature_encoders(attr) without return types