class_docstr: Implements test for Transforms that are performed for different devices
class_docstr_tok: ["Implements","test","for","Transforms","that","are","performed","for","different","devices"]
code: def test_convolve(self, cls, mode):
      leading_dims = (2, 3, 2)
      L_x, L_y = 32, 55
      x = torch.rand(
          *leading_dims, L_x, dtype=self.dtype, device=self.device
      )
      y = torch.rand(
          *leading_dims, L_y, dtype=self.dtype, device=self.device
      )
  
      convolve = getattr(T, cls)(mode=mode).to(
          device=self.device, dtype=self.dtype
      )
      output = convolve(x, y)
      ts_output = torch_script(convolve)(x, y)
      self.assertEqual(ts_output, output)
  
imports: import torch
  import torchaudio.transforms
  from parameterized import parameterized
  from torchaudio_unittest import common_utils
  from torchaudio_unittest.common_utils import skipIfRocm, TestBaseMixin, torch_script
label_desc: Use image node_id 30 for calling the Transforms obj's underlying member method code with example usage: obj.test_convolve(cls, mode) without return types