We need to replace the PyTorch reference `Model` with a custom CUDA kernel `ModelNew`. The reference computation: `torch.cumsum(x * mask, dim=self.dim)`, where `x` is fp32 (I think by default `torch.rand` gives fp32), mask is bool. Input shape: `[batch_size, input_shape] = [32768, 32768]`, so a 2D tensor of shape [32768, 32768]. `dim=1`. So we need to compute, for each row independently, the cumulative sum along dimension 1, but only including elements where mask is True (i.e., multiply x by mask, then cumsum). The reference does `x * mask`, where mask is bool, so mask gets promoted to same dtype as x, i.e., fp32, values 0.0 or 1.0, then cumsum along dim=1. So the core computation: for each i in [0, batch_size), for each j in [0, N), compute sum_{k=0..j} x[i,k] * mask[i,k]. We need a custom CUDA kernel that is faster than PyTorch's cumsum. Since the shape is huge: 32768x32768 = about 1.07 billion elements, ~4 GB of fp32 data. Cumsum along rows of a large 2D matrix. PyTorch's cumsum likely uses some parallel scan algorithm, but for such a large tensor it may be memory bandwidth bound. However, we also need to incorporate the mask multiplication. Doing that as a separate kernel (or fused) can save bandwidth. We need to write a CUDA kernel using torch.utils.cpp_extension.load_inline. Also need to consider if we can use cub::DeviceScan (CUB) for scan per row. Since we have many rows (32768), we can process each row with CUB block scan. The number of elements per row is also 32768. A 2D grid where each block handles a row (or multiple rows) using CUB's BlockScan. We need to get high memory bandwidth and occupancy. We can load elements from x and mask. Since mask is bool (1 byte each), we should load it as a vector or at least contiguous. Given row-major layout (dim=1 is contiguous? Actually a [32768, 32768] tensor in PyTorch default memory layout is row-major? PyTorch uses strided layout, but for a 2D tensor created by `torch.rand(batch_size, *input_shape)`, the strides are [32768, 1] (row-major). So dim=1 is the contiguous dimension. That means each row is contiguous in memory. This is good. So for each row i, elements x[i, j] are contiguous. We can assign one thread block per row, or multiple blocks per row. 32768 rows; 32768 elements per row. This is huge. Let's consider hardware. Let's get GPU specs first. We need to call get_gpu_specs. Then design kernel. We must write a complete Python module that defines ModelNew. At module load time, compile inline CUDA. Then instantiate ModelNew with same args as Model. We need to be careful: The evaluation may run on a specific GPU; we need to use `get_gpu_specs` to know its capabilities. Let's get GPU specs.