code: def scalerSingleValue(
      self, scaler_arg=("standard", StandardScaler())
  ):
      scaler_name, scaler = scaler_arg
  
      original = numpy.arange(1, dtype="float32")
  
      # Use Scipy to generate the expected results
      expected = scaler.fit_transform(original.reshape(-1, 1))
  
      # Add singleton dimensions to trainsform the input vector into a tensor
      original = numpy.expand_dims(original, axis=[0, 1, 2])
      result = TensorNormalize(scaler=scaler_name, axis=-1)(original)
  
      self.assertAlmostEqualVector(
          result.flatten(), expected.flatten(), 1e-6
      )
  
imports: from essentia_test import *
  from sklearn.preprocessing import MinMaxScaler, StandardScaler
label_desc: Use image node_id 1 for calling the TestTensorNormalize obj's underlying member method code with example usage: obj.scalerSingleValue(scaler_arg) without return types