code: def compute(model_path):
      now = time.monotonic()
      sess = rt.InferenceSession(model_path)
      input_det = sess.get_inputs()[0]
      label_det = sess.get_outputs()[0]
      input_name = input_det.name
      input_shape = input_det.shape
  
      if "N" in input_shape:
          input_shape[input_shape.index("N")] = 1
      input_shape[0] = 1
      print("input_shape", input_shape)
      t1 = time.monotonic() - now
      now = time.monotonic()
  
      for i in range(WARMUP):
          X_test = np.random.random(size=input_shape).astype(np.float32)
          pred_onx = sess.run(None, {input_name: X_test})[0]
      t2 = time.monotonic() - now
      now = time.monotonic()
      for i in range(ITER):
          X_test = np.random.random(size=input_shape).astype(np.float32)
          pred_onx = sess.run(None, {input_name: X_test})[0]
      t3 = time.monotonic() - now
      return t1, t2 / float(WARMUP), t3 / float(ITER)
  
imports: import onnxruntime
  import numpy
  import time
label_desc: Use image node_id 1 for calling a global function with example usage: compute(model_path) and returns: t1,  unknown,  unknown