ok.  I found to run CUDA_LAUNCH_BLOCKING=1 and got the following full traceback:
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-42-b6fff162d821> in <module>
     15             break
     16 
---> 17         y_pred = d_o_model(num, cat)
     18         single_loss = criterion(y_pred, label)
     19 

C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    539             result = self._slow_forward(*input, **kwargs)
    540         else:
--> 541             result = self.forward(*input, **kwargs)
    542         for hook in self._forward_hooks.values():
    543             hook_result = hook(self, input, result)

<ipython-input-39-a995c8522c32> in forward(self, x_categorical, x_numerical)
     57         #concatenating numerical and categorical columns
     58         x = torch.cat([x, x_numerical], 1)
---> 59         x = self.layers(x)
     60         #x = F.log_softmax(x)
     61         return x

C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    539             result = self._slow_forward(*input, **kwargs)
    540         else:
--> 541             result = self.forward(*input, **kwargs)
    542         for hook in self._forward_hooks.values():
    543             hook_result = hook(self, input, result)

C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\container.py in forward(self, input)
     90     def forward(self, input):
     91         for module in self._modules.values():
---> 92             input = module(input)
     93         return input
     94 

C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    539             result = self._slow_forward(*input, **kwargs)
    540         else:
--> 541             result = self.forward(*input, **kwargs)
    542         for hook in self._forward_hooks.values():
    543             hook_result = hook(self, input, result)

C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\batchnorm.py in forward(self, input)
     79             input, self.running_mean, self.running_var, self.weight, self.bias,
     80             self.training or not self.track_running_stats,
---> 81             exponential_average_factor, self.eps)
     82 
     83     def extra_repr(self):

C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\functional.py in batch_norm(input, running_mean, running_var, weight, bias, training, momentum, eps)
   1668     return torch.batch_norm(
   1669         input, weight, bias, running_mean, running_var,
-> 1670         training, momentum, eps, torch.backends.cudnn.enabled
   1671     )
   1672 

RuntimeError: cuDNN error: CUDNN_STATUS_MAPPING_ERROR

I’m a little more confused now.