code: def image_text_match(cropped_objects, text_query):
      transformed_images = [
          transform(image) for image in cropped_objects
      ]
      tokenized_text = tokenize([text_query])
      batch_images = paddle.stack(transformed_images)
      image_features = model.encode_image(batch_images)
      print("encode_image done!")
      text_features = model.encode_text(tokenized_text)
      print("encode_text done!")
      image_features /= image_features.norm(axis=-1, keepdim=True)
      text_features /= text_features.norm(axis=-1, keepdim=True)
      if len(text_features.shape) == 3:
          text_features = text_features.squeeze(0)
      probs = 100.0 * image_features @ text_features.T
      return F.softmax(probs[:, 0], axis=0)
  
imports: import os
  import cv2
  import time
  import sys
  import argparse
  import paddle
  import paddle.nn.functional
  import numpy
  from PIL import Image, ImageDraw
  import matplotlib.pyplot
  from segment_anything import sam_model_registry, SamAutomaticMaskGenerator
  from segment_anything.modeling.clip_paddle import build_clip_model, _transform
  from segment_anything.utils.sample_tokenizer import tokenize
  from paddleseg.utils.visualize import get_pseudo_color_map, get_color_map_list
label_desc: Use image node_id 3 for calling a global function with example usage: image_text_match(cropped_objects, text_query) and returns: F