Sorry for the late answer. Apparently I was mistaken and LinearTransformation will probably not work in this case.
If you are dealing with image data, the easiest approach would be to use transforms.functional.rotate 104.
However, this won’t work on arbitrary tensors.
Therefore, I’ve created a small (quick and dirty) example using a rotation matrix to rotate your tensor.
The first part uses nested for loops to perform the rotation and will most likely be slow. It’s just for the sake of debugging and I think the code is a bit easier to understand.
In the second part I’ve basically used the first code and used some matmuls to speed it up.
Note that I haven’t tested all edge cases, so please ping me, if you encounter any issues.
# Create dummy image
im = torch.zeros(1, 1, 10, 10)
im[:, :, :, 2] = 1.

# Set angle
angle = torch.tensor([72 * np.pi / 180.])

# Calculate rotation for each target pixel
x_mid = (im.size(2) + 1) / 2.
y_mid = (im.size(3) + 1) / 2.
im_rot = torch.zeros_like(im)
for r in range(im.size(2)):
    for c in range(im.size(3)):
        x = (r - x_mid) * torch.cos(angle) + (c - y_mid) * torch.sin(angle)
        y = -1.0 * (r - x_mid) * torch.sin(angle) + (c - y_mid) * torch.cos(angle)
        x = torch.round(x) + x_mid
        y = torch.round(y) + y_mid
        
        if (x >= 0 and y>=0 and x<im.size(2) and y<im.size(3)):
            im_rot[:, :, r, c] = im[:, :, x.long(), y.long()]


# Calculate rotation with inverse rotation matrix
rot_matrix = torch.tensor([[torch.cos(angle), torch.sin(angle)],
                            [-1.0*torch.sin(angle), torch.cos(angle)]])

# Use meshgrid for pixel coords
xv, yv = torch.meshgrid(torch.arange(im.size(2)), torch.arange(im.size(3)))
xv = xv.contiguous()
yv = yv.contiguous()
src_ind = torch.cat((
    (xv.float() - x_mid).view(-1, 1),
    (yv.float() - y_mid).view(-1, 1)),
    dim=1
)
    
# Calculate indices using rotation matrix
src_ind = torch.matmul(src_ind, rot_matrix.t())
src_ind = torch.round(src_ind)

src_ind += torch.tensor([[x_mid, y_mid]])

# Set out of bounds indices to limits
src_ind[src_ind < 0] = 0.
src_ind[:, 0][src_ind[:, 0] >= im.size(2)] = float(im.size(2)) - 1
src_ind[:, 1][src_ind[:, 1] >= im.size(3)] = float(im.size(3)) - 1
 
im_rot2 = torch.zeros_like(im)
src_ind = src_ind.long()
im_rot2[:, :, xv.view(-1), yv.view(-1)] = im[:, :, src_ind[:, 0], src_ind[:, 1]]
im_rot2 = im_rot2.view(1, 1, 10, 10)
print(im_rot)
print(im_rot2)