code: def get_clip_score(
      self, activations: np.ndarray, text_features: np.ndarray
  ) -> float:
      # Sizes should never mismatch, but if they do we want to compute
      # _some_ value instead of crash looping.
      size = min(len(activations), len(text_features))
      activations = activations[:size]
      text_features = text_features[:size]
  
      scores_out = []
      for i in range(0, len(activations), self.clip_score_batch_size):
          acts = activations[i : i + self.clip_score_batch_size]
          sub_features = text_features[
              i : i + self.clip_score_batch_size
          ]
          with torch.no_grad():
              image_features = self.clip_proj(
                  torch.from_numpy(acts).half().to(dist_util.dev())
              )
              image_features = image_features / image_features.norm(
                  dim=-1, keepdim=True
              )
              image_features = (
                  image_features.detach().cpu().float().numpy()
              )
          scores_out.extend(
              np.sum(sub_features * image_features, axis=-1).tolist()
          )
      return np.mean(scores_out) * self.clip_logit_scale
  
imports: from .inception_v3 import InceptionV3
  import blobfile
  import torch
  import torch.distributed
  import torch.nn
  from cm import dist_util
  import numpy
  import warnings
  from scipy import linalg
  from PIL import Image
  from tqdm import tqdm
label_desc: Use image node_id 5 for calling the FIDAndIS obj's underlying member method code with example usage: obj.get_clip_score(activations, text_features) and returns: unknown