code: def test_transform_thin_plate_spline_variable_input(self):
      import lasagne
      from lasagne.utils import floatX
      from theano.tensor import constant
  
      x = np.random.random((10, 3, 28, 28)).astype("float32")
      x_sym = theano.tensor.tensor4()
  
      l_in = lasagne.layers.InputLayer((None, 3, None, 28))
      l_loc = lasagne.layers.DenseLayer(
          lasagne.layers.ReshapeLayer(l_in, ([0], 3 * 28 * 28)),
          num_units=32,
      )
      l_trans = lasagne.layers.TPSTransformerLayer(
          l_in, l_loc, precompute_grid="auto"
      )
  
      # check that shape propagation works
      assert l_trans.output_shape[0] is None
      assert l_trans.output_shape[1] == 3
      assert l_trans.output_shape[2] is None
      assert l_trans.output_shape[3] == 28
  
      # check that data propagation works
      dest_offset = np.zeros(shape=(10, 32))
      inputs = floatX(np.arange(np.prod(x.shape)).reshape(x.shape))
      outputs = l_trans.get_output_for(
          [constant(inputs), constant(dest_offset)]
      ).eval()
      np.testing.assert_allclose(inputs, outputs, atol=5e-4)
  
imports: from mock import Mock
  import numpy
  import pytest
  import theano
  from lasagne.layers import InputLayer, standardize, get_output, get_all_params
  from lasagne.utils import floatX
label_desc: Use image node_id 2 for calling the TestTPSTransformLayer obj's underlying member method code with example usage: obj.test_transform_thin_plate_spline_variable_input() without return types