code: def scalerAlongAxis(
      self, axis=0, scaler_arg=("standard", StandardScaler())
  ):
      scaler_name, scaler = scaler_arg
  
      dims, length = 4, 2
      original = numpy.arange(length**dims, dtype="float32").reshape(
          [length] * dims
      )
  
      expected = numpy.empty(original.shape)
  
      for i in range(original.shape[0]):
          # Swap the axes before and after so we can test all the dimensions
          # using the same operator ([i, :, :, :]).
          original = numpy.swapaxes(original, 0, axis)
          expected = numpy.swapaxes(expected, 0, axis)
          tmp = scaler.fit_transform(
              original[i, :, :, :].reshape(-1, 1)
          )
          expected[i, :, :, :] = tmp.reshape(
              [1] + [length] * (dims - 1)
          )
          original = numpy.swapaxes(original, 0, axis)
          expected = numpy.swapaxes(expected, 0, axis)
  
      result = TensorNormalize(scaler=scaler_name, axis=axis)(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 3 for calling the TestTensorNormalize obj's underlying member method code with example usage: obj.scalerAlongAxis(axis, scaler_arg) without return types