class_docstr: Unit test class for testing scikit-learn converter.
code: def test_random(self):
      # Generate some random data_imputeValue.multiArrayValue[i]
      X = _np.random.random(size=(50, 3))
  
      for param in ("l1", "l2", "max"):
          cur_model = Normalizer(norm=param)
  
          output = cur_model.fit_transform(X)
  
          spec = converter.convert(cur_model, ["a", "b", "c"], "out")
  
          evaluate_transformer(
              spec,
              [dict(zip(["a", "b", "c"], row)) for row in X],
              [{"out": row} for row in output],
          )
  
file_path: coremltools/coremltools/test/sklearn_tests/test_normalizer.py
imports: import unittest
  import numpy
  from coremltools._deps import _HAS_SKLEARN
  from coremltools.models.utils import _is_macos, _macos_version, evaluate_transformer
label_desc: Use image node_id 1 for calling the NormalizerScikitTest obj's underlying member method code with example usage: obj.test_random() without return types