code: def test_get_output_for(self, axes, input_shape):
      rand_shape = [1] * len(input_shape)
      rand_shape[1] = 10
      # random input tensor
      input = np.random.randn(*input_shape).astype(
          theano.config.floatX
      ) + np.random.randn(*rand_shape).astype(theano.config.floatX)
  
      # create layer
      from lasagne.layers.normalization import StandardizationLayer
  
      layer = StandardizationLayer(input_shape, axes=axes)
  
      def get_exp_result(axis):
          input_mean = input.mean(axis=axis, keepdims=True)
          input_std = np.sqrt(
              input.var(axis=axis, keepdims=True) + layer.epsilon
          )
          return (input - input_mean) / input_std
  
      # choose normalization axes according to specification
      if axes == "auto" and len(input_shape) > 2:
          axes = tuple(range(2, len(input_shape)))
      elif axes == "auto" and len(input_shape) == 2:
          axes = (1,)
      elif axes == "spatial":
          axes = tuple(range(2, len(input_shape)))
      elif axes == "features":
          axes = tuple(range(1, len(input_shape)))
      else:
          axes = axes
  
      exp_result = get_exp_result(axes)
  
      kwargs = {"deterministic": True}
      result = layer.get_output_for(
          theano.tensor.constant(input), **kwargs
      ).eval()
  
      # compare expected results to actual results
      tol = {"atol": 1e-5, "rtol": 1e-6}
      assert np.allclose(result, exp_result, **tol)
  
imports: from mock import Mock
  import numpy
  import pytest
  import theano
label_desc: Use image node_id 4 for calling the TestStandardizationLayer obj's underlying member method code with example usage: obj.test_get_output_for(axes, input_shape) and returns: unknown