code: def test_with_nones(
      self, DummyInputLayer, input, kernel, output, kwargs
  ):
      if kwargs.get("untie_biases", False):
          pytest.skip()
      from lasagne.layers import TransposedConv2DLayer
  
      b, c, h, w = input.shape
      input_layer = DummyInputLayer((None, c, None, None))
      layer = TransposedConv2DLayer(
          input_layer,
          num_filters=kernel.shape[0],
          filter_size=kernel.shape[2:],
          W=kernel.transpose(1, 0, 2, 3),
          **kwargs,
      )
      if "output_size" not in kwargs or isinstance(
          kwargs["output_size"], T.Variable
      ):
          assert layer.output_shape == (
              None,
              output.shape[1],
              None,
              None,
          )
      actual = layer.get_output_for(input).eval()
      assert actual.shape == output.shape
      assert np.allclose(actual, output)
      # Check get_output_shape_for for non symbolic output
      if "output_size" in kwargs and not isinstance(
          kwargs["output_size"], T.Variable
      ):
          assert layer.get_output_shape_for(input.shape) == output.shape
          # The layer should report the output size even when it
          # doesn't know most of the input size
          assert (
              layer.output_shape
              == (None, output.shape[1]) + kwargs["output_size"]
          )
  
imports: import numpy
  import pytest
  import importlib
  import theano
  from theano import tensor
  import lasagne
  from lasagne.utils import floatX, as_tuple
label_desc: Use image node_id 2 for calling the TestTransposedConv2DLayer obj's underlying member method code with example usage: obj.test_with_nones(DummyInputLayer, input, kernel, output, kwargs) without return types