code: def test_paddle_conv2d_tranpose(
      *,
      dtype_vals,
      on_device,
      fn_tree,
      frontend,
      test_flags,
      backend_fw,
  ):
      (
          dtype,
          vals,
          weight,
          bias,
          dilations,
          strides,
          padding,
          output_pad,
          fc,
      ) = dtype_vals
      dilations = (
          1  # ToDo: remove this when support for dilation > 1 is added
      )
      assume(
          all(
              x > 0
              for x in _output_shape(
                  2,
                  dilations,
                  strides,
                  padding,
                  output_pad,
                  vals.shape,
                  weight.shape,
              )
          )
      )
      helpers.test_frontend_function(
          input_dtypes=dtype,
          backend_to_test=backend_fw,
          frontend=frontend,
          test_flags=test_flags,
          fn_tree=fn_tree,
          on_device=on_device,
          x=vals,
          weight=weight,
          bias=bias,
          stride=strides,
          padding=padding,
          output_padding=output_pad,
          dilation=dilations,
          groups=fc,
      )
  
file_path: ivy/ivy_tests/test_ivy/test_frontends/test_paddle/test_nn/test_functional/test_conv.py
imports: from hypothesis import assume
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
  from ivy_tests.test_ivy.test_frontends.test_torch.test_nn.test_functional.test_convolution_functions import _x_and_filters, _output_shape
  from ivy_tests.test_ivy.test_functional.test_nn.test_layers import _assume_tf_dilation_gt_1
label_desc: Use image node_id 4 for calling a global function with example usage: test_paddle_conv2d_tranpose() without return types