code: def test_tflite_different_io_qnn_params():
      scale_and_zp = {
          "lhs_scale": 0.0156863,
          "lhs_zp": 127,
          "rhs_scale": 0.0117647,
          "rhs_zp": 85,
          "output_scale": 0.0235294,
          "output_zp": 128,
      }
      x_datas = [
          np.array((76, 140, 153, 172)).reshape((1, 4)),
          np.array((133, 140, 146, 153)).reshape((1, 4)),
          np.array((76, 140, 172, 146)).reshape((1, 4)),
      ]
      y_datas = [
          np.array((136, 119, 128, 17)).reshape((1, 4)),
          np.array((136, 119, 111, 94)).reshape((1, 4)),
          np.array((136, 119, 17, 128)).reshape((1, 4)),
      ]
      golden_outputs = [
          np.array((68, 120, 123, 192)).reshape((1, 4)),
          np.array((106, 120, 128, 140)).reshape((1, 4)),
          np.array((68, 120, 192, 119)).reshape((1, 4)),
      ]
      qnn_subtract_driver(
          x_datas, y_datas, golden_outputs, scale_and_zp
      )
  
imports: import tvm
  import numpy
  from tvm import relay
label_desc: Use image node_id 3 for calling a global function with example usage: test_tflite_different_io_qnn_params() without return types