code: def process_feature_embedding(
      self,
      attr: str,
      sample_list: Dict[str, Any],
      text_embedding_total: torch.Tensor,
      text_embedding_vec: torch.Tensor,
      extra: list = [],
      batch_size_t: Optional[int] = None,
  ):
      batch_size_t = (
          sample_list.get_batch_size()
          if batch_size_t is None
          else batch_size_t
      )
  
      # Convert list of keys to the actual values
      if hasattr(sample_list, "image"):
          feature = sample_list.image
  
          feature_encoder = getattr(self, attr + "_feature_encoders")
          encoded_feature = feature_encoder(feature)
          b, c, h, w = encoded_feature.shape
          padded_feat = torch.zeros(
              (b, c, 32, 32),
              dtype=torch.float,
              device=encoded_feature.device,
          )
          padded_feat[:, :, :h, :w] = encoded_feature
          encoded_feature = padded_feat
      else:
          feature = sample_list.image_feature_0
  
          feature_encoder = getattr(self, attr + "_feature_encoders")
          encoded_feature = feature_encoder(feature)
  
      feature_embedding = getattr(
          self, attr + "_feature_embeddings_list"
      )
      feature_sga, feature_cbn = feature_embedding(
          encoded_feature,
          text_embedding_total,
          text_embedding_vec,
          None,
          sample_list.text_mask,
      )
  
      return feature_sga, feature_cbn
  
imports: import copy
  from typing import Any, Dict, List, Optional, Tuple
  import omegaconf
  import torch
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.modules.embeddings import PreExtractedEmbedding, TextEmbedding, TwoBranchEmbedding
  from mmf.modules.layers import BranchCombineLayer, ClassifierLayer
  from mmf.utils.build import build_image_encoder
  from mmf.utils.general import filter_grads
  from omegaconf import DictConfig
label_desc: Use image node_id 17 for calling the MoVieMcan obj's underlying member method code with example usage: obj.process_feature_embedding(attr, sample_list, text_embedding_total, text_embedding_vec, extra, batch_size_t) and returns: feature_sga,  feature_cbn