code: def _init_feature_embeddings(self, attr):
      feature_embeddings_list = []
      num_feature_feat = len(
          getattr(self.config, f"{attr}_feature_encodings")
      )
  
      self.feature_embeddings_out_dim = 0
  
      for _ in range(num_feature_feat):
          feature_embeddings = []
          feature_attn_model_list = self.config[
              attr + "_feature_embeddings"
          ]
  
          for feature_attn_model_params in feature_attn_model_list:
              feature_embedding = ImageFeatureEmbedding(
                  getattr(self, attr + "_feature_dim"),
                  self.text_embeddings_out_dim,
                  **feature_attn_model_params,
              )
              feature_embeddings.append(feature_embedding)
              self.feature_embeddings_out_dim += (
                  feature_embedding.out_dim
              )
  
          feature_embeddings = nn.ModuleList(feature_embeddings)
          feature_embeddings_list.append(feature_embeddings)
  
      self.feature_embeddings_out_dim *= getattr(
          self, attr + "_feature_dim"
      )
  
      setattr(
          self,
          attr + "_feature_embeddings_out_dim",
          self.feature_embeddings_out_dim,
      )
      del self.feature_embeddings_out_dim
      setattr(
          self,
          attr + "_feature_embeddings_list",
          nn.ModuleList(feature_embeddings_list),
      )
  
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 9 for calling the Pythia obj's underlying member method code with example usage: obj._init_feature_embeddings(attr) without return types