Fix: swap input_amplitude and target_amplitude in JukeboxLoss.forward (#8821)

Fixes #8820

### Description

In `JukeboxLoss.forward()`, the variable names `input_amplitude` and
`target_amplitude` were swapped:
- `input_amplitude` was computed from `target` (should be from `input`)
- `target_amplitude` was computed from `input` (should be from `target`)

This fix corrects the assignments to match semantic meaning and the
standard `forward(input, target)` PyTorch convention.

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [x] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: Zeeshan Modi <92383127+Zeesejo@users.noreply.github.com>

Author: Zeesejo

monai/losses/spectral_loss.py

@@ -55,8 +55,8 @@ def __init__(
         self.fft_norm = fft_norm
 
     def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
-        input_amplitude = self._get_fft_amplitude(target)
-        target_amplitude = self._get_fft_amplitude(input)
+        input_amplitude = self._get_fft_amplitude(input)
+        target_amplitude = self._get_fft_amplitude(target)
 
         # Compute distance between amplitude of frequency components
         # See Section 3.3 from https://arxiv.org/abs/2005.00341

monai/losses/ssim_loss.py

@@ -111,17 +111,17 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
                 # 2D data
                 x = torch.ones([1,1,10,10])/2
                 y = torch.ones([1,1,10,10])/2
-                print(1-SSIMLoss(spatial_dims=2)(x,y))
+                print(SSIMLoss(spatial_dims=2)(x,y))
 
                 # pseudo-3D data
                 x = torch.ones([1,5,10,10])/2  # 5 could represent number of slices
                 y = torch.ones([1,5,10,10])/2
-                print(1-SSIMLoss(spatial_dims=2)(x,y))
+                print(SSIMLoss(spatial_dims=2)(x,y))
 
                 # 3D data
                 x = torch.ones([1,1,10,10,10])/2
                 y = torch.ones([1,1,10,10,10])/2
-                print(1-SSIMLoss(spatial_dims=3)(x,y))
+                print(SSIMLoss(spatial_dims=3)(x,y))
         """
         ssim_value = self.ssim_metric._compute_tensor(input, target).view(-1, 1)
         loss: torch.Tensor = 1 - ssim_value