code: def test_defaults(
      self, DummyInputLayer, input, kernel, output, kwargs
  ):
      from lasagne.layers import TransposedConv2DLayer
  
      b, c, h, w = input.shape
      input_layer = DummyInputLayer((b, c, h, w))
      layer = TransposedConv2DLayer(
          input_layer,
          num_filters=kernel.shape[0],
          filter_size=kernel.shape[2:],
          W=kernel.transpose(1, 0, 2, 3),
          **kwargs,
      )
      actual = layer.get_output_for(input).eval()
      assert actual.shape == output.shape
      # layer.output_shape == actual.shape or None
      assert all(
          [
              s1 == s2
              for (s1, s2) in zip(actual.shape, output.shape)
              if s2
          ]
      )
      assert np.allclose(actual, output)
      # Check get_output_shape_for for symbolic output
      if "output_size" in kwargs and isinstance(
          kwargs["output_size"], T.Variable
      ):
          assert all(
              el is None
              for el in layer.get_output_shape_for(input.shape)[2:]
          )
  
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 1 for calling the TestTransposedConv2DLayer obj's underlying member method code with example usage: obj.test_defaults(DummyInputLayer, input, kernel, output, kwargs) without return types