class_docstr: Unit test class for testing scikit-learn converter.
code: def test_random(self):
      # Generate some random data
      X = _np.random.random(size=(50, 3))
  
      cur_model = StandardScaler()
  
      output = cur_model.fit_transform(X)
  
      spec = converter.convert(
          cur_model, ["a", "b", "c"], "out"
      ).get_spec()
  
      metrics = evaluate_transformer(
          spec,
          [dict(zip(["a", "b", "c"], row)) for row in X],
          [{"out": row} for row in output],
      )
  
      assert metrics["num_errors"] == 0
  
file_path: coremltools/coremltools/test/sklearn_tests/test_standard_scalar.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 StandardScalerTestCase obj's underlying member method code with example usage: obj.test_random() without return types