code: def test_leaky_relu(dtype, shape, alpha):
      """Compare Leaky ReLU output with TVM."""
  
      np.random.seed(0)
  
      iinfo = np.iinfo(dtype)
      zp_min = iinfo.min
      zp_max = iinfo.max
      input_zp = zp_min + 128
      input_sc = 0.0068132
      output_zp = (
          zp_min + 126
      )  # values offset more than 126 can cause saturation
      output_sc = 0.0078125
  
      inputs = {
          "x": tvm.nd.array(
              np.random.randint(
                  zp_min, high=zp_max, size=shape, dtype=dtype
              )
          )
      }
      outputs = []
      for npu in [False, True]:
          model = _get_model(
              shape,
              input_zp,
              input_sc,
              output_zp,
              output_sc,
              dtype,
              alpha,
          )
          mod = tei.make_module(model, [])
          outputs.append(
              tei.build_and_run(
                  mod,
                  inputs,
                  1,
                  {},
                  npu=npu,
                  additional_config_args={
                      "inline_non_compute_intensive_partitions": False
                  },
              )
          )
  
      tei.verify(outputs, dtype, 1)
  
file_path: tvm/tests/python/contrib/test_ethosn/test_leaky_relu.py
imports: import pytest
  import numpy
  import tvm
  from tvm import relay
  from tvm.testing import requires_ethosn
  from .None import infrastructure
label_desc: Use image node_id 2 for calling a global function with example usage: test_leaky_relu(dtype, shape, alpha) without return types