code: def test_instance_norm_model_helper(
      self, N, C, H, W, order, epsilon, seed, is_test
  ):
      np.random.seed(seed)
      model = model_helper.ModelHelper(name="test_model")
      brew.instance_norm(
          model,
          "input",
          "output",
          C,
          epsilon=epsilon,
          order=order,
          is_test=is_test,
      )
  
      input_blob = np.random.rand(N, C, H, W).astype(np.float32)
      if order == "NHWC":
          input_blob = utils.NCHW2NHWC(input_blob)
  
      self.ws.create_blob("input").feed(input_blob)
  
      self.ws.create_net(model.param_init_net).run()
      self.ws.create_net(model.net).run()
  
      if is_test:
          scale = self.ws.blobs["output_s"].fetch()
          assert scale is not None
          assert scale.shape == (C,)
          bias = self.ws.blobs["output_b"].fetch()
          assert bias is not None
          assert bias.shape == (C,)
  
      output_blob = self.ws.blobs["output"].fetch()
      if order == "NHWC":
          output_blob = utils.NHWC2NCHW(output_blob)
  
      assert output_blob.shape == (N, C, H, W)
  
file_path: pytorch/caffe2/python/operator_test/instance_norm_test.py
imports: import numpy
  from hypothesis import given, assume, settings
  import hypothesis.strategies
  from caffe2.python import core, model_helper, brew, utils
  import caffe2.python.hypothesis_test_util
  import caffe2.python.serialized_test.serialized_test_util
  import unittest
label_desc: Use image node_id 8 for calling the TestInstanceNorm obj's underlying member method code with example usage: obj.test_instance_norm_model_helper(N, C, H, W, order, epsilon, seed, is_test) without return types