code: def test_deconv2d_output_shape(
      input_dim, kernel_size, stride, padding, output_padding, dilation
  ):
      if (
          output_padding[0] >= stride[0]
          or output_padding[0] >= dilation[0]
          or output_padding[1] >= stride[1]
          or output_padding[1] >= dilation[1]
      ):
          # skip invalid cases
          return
      h, w = convtransp2d_output_shape(
          input_dim,
          kernel_size=kernel_size,
          stride=stride,
          pad=padding,
          dilation=dilation,
          out_pad=output_padding,
      )
      deconv = torch.nn.ConvTranspose2d(
          1,
          1,
          kernel_size,
          stride=stride,
          padding=padding,
          output_padding=output_padding,
          dilation=dilation,
      )
      x = torch.rand(1, 1, *input_dim)
      assert deconv(x).shape[2:] == (h, w)
  
imports: import pytest
  import torch
  from espnet2.enh.layers.conv_utils import conv2d_output_shape, convtransp2d_output_shape
label_desc: Use image node_id 2 for calling a global function with example usage: test_deconv2d_output_shape(input_dim, kernel_size, stride, padding, output_padding, dilation) without return types