hey, can you check and see what is wrong with this? import numpy as np
import pycuda.driver as cuda
import pycuda.autoinit
from pycuda.compiler import SourceModule

# Define input parameters
batch_size = 1
input_channel = 1
input_height = 5
input_width = 5
output_channel = 1
filter_height = 3
filter_width = 3
stride_height = 1
stride_width = 1
output_height = (input_height - filter_height) // stride_height + 1
output_width = (input_width - filter_width) // stride_width + 1

# Create input data and filter weights
inptr = np.arange(1, batch_size * input_channel * input_height * input_width + 1, dtype=np.float32)
filptr = np.arange(1, output_channel * input_channel * filter_height * filter_width + 1, dtype=np.float32)

# Expected output computed manually or using a reliable library
expected_output = np.array([411, 456, 501,
                            666, 711, 756,
                            921, 966, 1011], dtype=np.float32)

# Allocate memory on the device
d_inptr = cuda.mem_alloc(inptr.nbytes)
d_filptr = cuda.mem_alloc(filptr.nbytes)
d_outptr = cuda.mem_alloc(expected_output.nbytes)

# Copy data to the device
cuda.memcpy_htod(d_inptr, inptr)
cuda.memcpy_htod(d_filptr, filptr)

# Define dimensions
d_dims_a = np.array([batch_size, input_channel, input_height, input_width], dtype=np.uint64)
d_dims_b = np.array([output_channel, input_channel, filter_height, filter_width], dtype=np.uint64)
d_dims_c = np.array([batch_size, output_channel, output_height, output_width], dtype=np.uint64)

d_dims_a_gpu = cuda.mem_alloc(d_dims_a.nbytes)
d_dims_b_gpu = cuda.mem_alloc(d_dims_b.nbytes)
d_dims_c_gpu = cuda.mem_alloc(d_dims_c.nbytes)

cuda.memcpy_htod(d_dims_a_gpu, d_dims_a)
cuda.memcpy_htod(d_dims_b_gpu, d_dims_b)
cuda.memcpy_htod(d_dims_c_gpu, d_dims_c)

# Define the block and grid sizes
BLOCKSIZE = 16
grid_x = (output_height + BLOCKSIZE - 1) // BLOCKSIZE
grid_y = (output_width + BLOCKSIZE - 1) // BLOCKSIZE
grid_z = batch_size * output_channel

# Compile the kernel
mod = SourceModule("""
template< int BLOCKSIZE >
__global__ void im2winKernelConvNCHWBASE(
    float* __restrict__ inptr,
    float* __restrict__ filptr,
    float* __restrict__ outptr,
    const size_t* __restrict__ d_dims_a, // input dimensions
    const size_t* __restrict__ d_dims_b, // filter dimensions
    const size_t* __restrict__ d_dims_c  // output dimensions
) {
    size_t tid = threadIdx.x;
    size_t Idx_x = blockIdx.x * BLOCKSIZE + tid % BLOCKSIZE; // Output height index (i)
    size_t Idx_y = blockIdx.y * BLOCKSIZE + tid / BLOCKSIZE; // Output width index (j)
    
    // Compute batch index (n) and output channel index (k)
    size_t Idx = blockIdx.z;
    size_t output_batch = d_dims_c[0];
    size_t output_channel = d_dims_c[1];
    size_t Idx_n = Idx / output_channel; // Batch index
    size_t Idx_k = Idx % output_channel; // Output channel index (k)

    size_t input_batch = d_dims_a[0];
    size_t input_channel = d_dims_a[1];
    size_t input_height = d_dims_a[2];
    size_t input_width = d_dims_a[3];

    size_t filter_height = d_dims_b[2];
    size_t filter_width = d_dims_b[3];

    size_t output_height = d_dims_c[2];
    size_t output_width = d_dims_c[3];

    // Typically, stride is a parameter or computed based on padding and dimensions
    size_t stride_height = 1; // Should be passed as a parameter
    size_t stride_width = 1;  // Should be passed as a parameter

    if (Idx_x >= output_height || Idx_y >= output_width) return;

    size_t indexY = Idx_n * output_channel * output_height * output_width
                    + Idx_k * output_height * output_width
                    + Idx_x * output_width
                    + Idx_y;

    float tmp = 0.0f;

    for (size_t c = 0; c < input_channel; ++c) {
        for (size_t h = 0; h < filter_height; ++h) {
            for (size_t w = 0; w < filter_width; ++w) {
                size_t in_i = Idx_x * stride_height + h;
                size_t in_j = Idx_y * stride_width + w;
                if (in_i >= input_height || in_j >= input_width) continue;

                size_t indexD = Idx_n * input_channel * input_height * input_width
                                + c * input_height * input_width
                                + in_i * input_width
                                + in_j;

                size_t indexW = Idx_k * input_channel * filter_height * filter_width
                                + c * filter_height * filter_width
                                + h * filter_width + w;

                tmp += inptr[indexD] * filptr[indexW];
            }
        }
    }
    outptr[indexY] = tmp;
}
"""
, options=['-use_fast_math'])

kernel = mod.get_function("im2winKernelConvNCHWBASE")

# Run the kernel
kernel(d_inptr, d_filptr, d_outptr, d_dims_a_gpu, d_dims_b_gpu, d_dims_c_gpu,
       block=(BLOCKSIZE*BLOCKSIZE, 1, 1),
       grid=(grid_x, grid_y, grid_z))

# Copy the result back to the host
outptr = np.empty_like(expected_output)
cuda.memcpy_dtoh(outptr, d_outptr)

# Compare the kernel output with the expected output
print("Computed Output:")
print(outptr.reshape((output_height, output_width)))

print("\nExpected Output:")
print(expected_output.reshape((output_height, output_width)))

# Verify correctness
if np.allclose(outptr, expected_output):
    print("\nTest passed!")
else:
    print("\nTest failed!")pycuda.driver.CompileError: nvcc compilation of C:\Users\Biscu\AppData\Local\Temp\tmpn8cj2yvi\kernel.cu failed
[command: nvcc --cubin -use_fast_math -arch sm_61 -m64 -IC:\Users\Biscu\AppData\Local\Programs\Python\Python310\lib\site-packages\pycuda\cuda kernel.cu]
[stdout:
kernel.cu(3): error: this declaration may not have extern "C" linkage

1 error detected in the compilation of "kernel.cu".
kernel.cu I think it has to do with template parameters