code: def _prepare_inference_request(self):
      # The digits dataset
      digits = datasets.load_digits()
  
      # To apply a classifier on this data,
      # we need to flatten the image, to
      # turn the data in a (samples, feature) matrix:
      n_samples = len(digits.images)
      data = digits.images.reshape((n_samples, -1))
  
      # Split data into train and test subsets
      _, X_test, _, _ = train_test_split(
          data, digits.target, test_size=0.5, shuffle=False
      )
  
      x_0 = X_test[0:1]
      self.inference_request = {
          "inputs": [
              {
                  "name": "predict",
                  "shape": x_0.shape,
                  "datatype": "FP32",
                  "data": x_0.tolist(),
              }
          ]
      }
  
file_path: pytorch-lightning/docs/source-app/examples/model_server_app/locustfile.py
imports: from locust import FastHttpUser, task
  from sklearn import datasets
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 3 for calling the HelloWorldUser obj's underlying member method code with example usage: obj._prepare_inference_request() without return types