Add complex dtype support in functional autograd test (pytorch#2244)

Summary:
In autograd tests, to guarantee the precision, the dtype of Tensors are converted to `torch.float64` if they are real. However, the complex dtype is not considered. This PR adds `self.complex_dtype` support to the inputs.

Pull Request resolved: pytorch#2244

Reviewed By: mthrok

Differential Revision: D34272998

Pulled By: nateanl

fbshipit-source-id: e8698a74d7b8d99ee0fcb5f5cb5f2ffc8c80b9b5

test/torchaudio_unittest/functional/autograd_impl.py

@@ -24,7 +24,7 @@ def assert_grad(
         inputs_ = []
         for i in inputs:
             if torch.is_tensor(i):
-                i = i.to(dtype=self.dtype, device=self.device)
+                i = i.to(dtype=self.complex_dtype if i.is_complex() else self.dtype, device=self.device)
                 if enable_all_grad:
                     i.requires_grad = True
             inputs_.append(i)