Fixing linter issues

Author: prabhuomkar

tutorials/popular/blitz/autograd/main.cpp

@@ -8,7 +8,7 @@ int main() {
     std::cout << "Autograd: Automatic Differentiation\n\n";
 
     std::cout << "Tensor\n\n";
-    
+
     // Create a tensor and set requires_grad=True to track computation with it:
     auto x = torch::ones({2, 2}, torch::TensorOptions().requires_grad(true));
     std::cout << "x:\n" << x << '\n';
@@ -38,7 +38,7 @@ int main() {
 
     // Let’s backprop now:
     out.backward();
-    
+
     // Print gradients d(out)/dx:
     std::cout << "x.grad:\n" << x.grad() << '\n';
 

tutorials/popular/blitz/tensors/main.cpp

@@ -8,11 +8,11 @@ int main() {
     std::cout << "What is PyTorch?\n\n";
 
     std::cout << "Tensors\n\n";
-    
+
     // Construct a 5x3 matrix, uninitialized:
     auto x = torch::empty({5, 3});
     std::cout << "x:\n" << x << '\n';
-    
+
     // Construct a randomly initialized matrix:
     x = torch::rand({5, 3});
     std::cout << "x:\n" << x << '\n';
@@ -57,9 +57,9 @@ int main() {
     // Resizing: If you want to resize/reshape tensor, you can use torch::view:
     x = torch::randn({4, 4});
     y = x.view(16);
-    auto z = x.view({-1, 8}); // the size -1 is inferred from other dimensions
+    auto z = x.view({-1, 8});  // the size -1 is inferred from other dimensions
     std::cout << x.element_size() << y.element_size() << z.element_size() << '\n';
-    
+
     // If you have a one element tensor, use .item() to get the value as a Python number
     x = torch::randn(1);
     std::cout << "x:\n" << x << '\n';