torch.nn.functional.l1_loss computes a criterion with the MSE, not the MAE

The L1Loss should create a criterion that measures the MAE error, but in TorchSharp it measures the MSE.

I believe that this is incorrect. See the following screenshot comparing TorchSharp and pytorch, which I believe should get identical results, but don't:

See the call to functional::mse_loss:

TorchSharp/src/Native/LibTorchSharp/THSLoss.cpp

Lines 94 to 102 in 26240d5

    
           Tensor THSNN_l1_loss(const Tensor input, const Tensor target, const int64_t reduction) 
        
           { 
        
               CATCH_RETURN_Tensor( 
        
                   auto opts = torch::nn::functional::MSELossFuncOptions(); 
        
                   ApplyReduction(opts, reduction); 
        
                   res = ResultTensor(torch::nn::functional::mse_loss(*input, *target, opts)); 
        
               ) 
        
           }

If this is indeed incorrect, I would be happy to submit a fix in a PR, but am still figuring out how to make my projects use the locally built version of TorchSharp—I'm not the biggest C# / dotnet / nuget expert.

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	Tensor THSNN_l1_loss(const Tensor input, const Tensor target, const int64_t reduction)
	{
	CATCH_RETURN_Tensor(
	auto opts = torch::nn::functional::MSELossFuncOptions();
	ApplyReduction(opts, reduction);

	res = ResultTensor(torch::nn::functional::mse_loss(input, target, opts));
	)
	}

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