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  import numpy as np
  import tritonclient.http as httpclient
  from PIL import Image
  from torchvision import transforms
  from tritonclient.utils import triton_to_np_dtype
  
  
  # preprocessing function
  def rn50_preprocess(img_path="/triton_example/img1.jpg"):
      img = Image.open(img_path)
      preprocess = transforms.Compose(
          [
              transforms.Resize(256),
              transforms.CenterCrop(224),
              transforms.ToTensor(),
              transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
          ]
      )
      return preprocess(img).unsqueeze(0).numpy()
  
  
  transformed_img = rn50_preprocess()
  
  # Setting up client
  client = httpclient.InferenceServerClient(url="localhost:8000")
  
  inputs = httpclient.InferInput("x", transformed_img.shape, datatype="FP32")
  inputs.set_data_from_numpy(transformed_img, binary_data=True)
  
  outputs = httpclient.InferRequestedOutput("output0", binary_data=True, class_count=1000)
  
  # Querying the server
  results = client.infer(model_name="resnet50", inputs=[inputs], outputs=[outputs])
  inference_output = results.as_numpy("output0")
  print(inference_output[:5])
  