RESOLVED: resolved it the issue, disc() (fc1) took size [32, 784] and I had the input tensor (adv_ex) as [32,28,28]
Receiving a runtime error and I’m not sure why this line is causing it.
Here is the code block:
for epoch in range(num_epochs):
    for batch_idx, (real, labels) in enumerate(loader):
        #get a fixed input batch to display gen output
        if batch_idx == 0:
            if epoch == 0:
                fixed_input = real.view(-1,784).to(device)
        
        adv_ex = real.clone().reshape(-1,784).to(device) # [32, 784] advex copy of first batch flattened
        real = real.view(-1, 784).to(device) # [32, 784] # real batch flattened
        labels = labels.to(device) # size() [32] 32 labels in batch
        
        
        #purturb each image in adv_ex
        tmp_adv_ex = []
        for idx, item in enumerate(adv_ex):
            purturbation = gen(adv_ex[idx])
            tmp_adv_ex.append(adv_ex[idx] + purturbation)
        adv_ex = torch.cat(tmp_adv_ex, dim=0)
        adv_ex = real.clone().reshape(-1,784).to(device)
        
         
        ### Train Generator: min log(1 - D(G(z))) <-> max log(D(G(z))
        output = disc(adv_ex).view(-1)
        lossG = torch.mean(torch.log(1. - output)) #get loss for gen's desired desc pred

        adv_ex = adv_ex.reshape(-1,1,28,28)
        f_pred = target(adv_ex)
        f_loss = CE_loss(f_pred, labels) #add loss for gens desired f pred
        loss_G_Final = f_loss+lossG # can change the weight of this loss term later

        opt_gen.zero_grad()
        loss_G_Final.backward()
        opt_gen.step()
        
        ### Train Discriminator: max log(D(x)) + log(1 - D(G(z)))
        
        disc_real = disc(real).view(-1)
        disc_fake = disc(adv_ex).view(-1)
        lossD = -torch.mean(torch.log(disc(real)) + torch.log(1. - disc(adv_ex)))
        # can decide later how much that loss term weighs
        
        opt_disc.zero_grad()
        lossD.backward(retain_graph=True)
        opt_disc.step()

This is the error code:
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
/tmp/ipykernel_25258/819410292.py in <module>
     47         ### Train Discriminator: max log(D(x)) + log(1 - D(G(z)))
     48         disc_real = disc(real).view(-1)
---> 49         disc_fake = disc(adv_ex).view(-1)
     50         lossD = -torch.mean(torch.log(disc(real)) + torch.log(1. - disc(adv_ex)))
     51         # can decide later how much that loss term weighs

~/.conda/envs/mypytorch19/lib/python3.9/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1050                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051             return forward_call(*input, **kwargs)
   1052         # Do not call functions when jit is used
   1053         full_backward_hooks, non_full_backward_hooks = [], []

~/AdvGan9-4/models.py in forward(self, x)
     28 
     29     def forward(self, x):
---> 30         x = self.fc1(x)
     31         x = nn.ReLU()(x)
     32         x = self.fc2(x)

~/.conda/envs/mypytorch19/lib/python3.9/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1050                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051             return forward_call(*input, **kwargs)
   1052         # Do not call functions when jit is used
   1053         full_backward_hooks, non_full_backward_hooks = [], []

~/.conda/envs/mypytorch19/lib/python3.9/site-packages/torch/nn/modules/linear.py in forward(self, input)
     94 
     95     def forward(self, input: Tensor) -> Tensor:
---> 96         return F.linear(input, self.weight, self.bias)
     97 
     98     def extra_repr(self) -> str:

~/.conda/envs/mypytorch19/lib/python3.9/site-packages/torch/nn/functional.py in linear(input, weight, bias)
   1845     if has_torch_function_variadic(input, weight):
   1846         return handle_torch_function(linear, (input, weight), input, weight, bias=bias)
-> 1847     return torch._C._nn.linear(input, weight, bias)
   1848 
   1849 

RuntimeError: CUDA error: CUBLAS_STATUS_INVALID_VALUE when calling `cublasSgemm( handle, opa, opb, m, n, k, &alpha, a, lda, b, ldb, &beta, c, ldc)`

 ** On entry to SGEMM  parameter number 10 had an illegal value


How should I interpret this and how should I modify line 49 to resolve it?