input_type = torch.randn(1, 3, 224, 224, requires_grad=True).cuda()

This line of code creates a 4-dimensional tensor of size 1x3x224x224 using PyTorch's `torch.randn()` function. The `1` in the first dimension represents the batch size (i.e., one input sample), `3` represents the number of input channels (e.g., RGB channels) and `224` represents the height and width of the input image. The `requires_grad=True` argument tells PyTorch to track the gradient of this tensor during backpropagation, which is necessary for training neural networks using gradient-based optimization algorithms. Finally, the `.cuda()` method moves the tensor to the GPU for faster computation if available.