code: def forward(self, image, text):
      text_features = self.encode_text(text)[None, :]
      image_features = self.encode_image(image)
  
      image_features = image_features / image_features.norm(
          axis=1, keepdim=True
      )
      text_features = text_features / text_features.norm(
          axis=1, keepdim=True
      )
      # cosine similarity as logits
      logits_per_image = image_features @ text_features.t()
      logits_per_text = logits_per_image.t()
      return logits_per_image, logits_per_text
  
imports: from collections import OrderedDict
  from typing import Tuple, Union
  import numpy
  import paddle
  from paddle.nn.initializer import Constant
  from paddle.vision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
label_desc: Use image node_id 6 for calling the CLIP obj's underlying member method code with example usage: obj.forward(image, text) and returns: logits_per_image,  logits_per_text