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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def gpu_iuwt_decomposition(in1, scale_count, scale_adjust, store_smoothed, store_on_gpu):
 """ This function calls the a trous algorithm code to decompose the input into its wavelet coefficients. This is the isotropic undecimated wavelet transform implemented for a GPU. INPUTS: in1 (no default):
 Array on which the decomposition is to be performed. scale_count (no default):
 Maximum scale to be considered. scale_adjust (no default):
 Adjustment to scale value if first scales are of no interest. store_smoothed (no default):
 Boolean specifier for whether the smoothed image is stored or not. store_on_gpu (no default):
 Boolean specifier for whether the decomposition is stored on the gpu or not. OUTPUTS: detail_coeffs Array containing the detail coefficients. C0 (optional):
 Array containing the smoothest version of the input. """


    # The following simple kernel just allows for the construction of a 3D decomposition on the GPU.

    ker = SourceModule("""
                        __global__ void gpu_store_detail_coeffs(float *in1, float *in2, float* out1, int *scale, int *adjust)
                        {
                            const int len = gridDim.x*blockDim.x;
                            const int i = (blockDim.x * blockIdx.x + threadIdx.x);
                            const int j = (blockDim.y * blockIdx.y + threadIdx.y)*len;
                            const int k = (blockDim.z * blockIdx.z + threadIdx.z)*(len*len);
                            const int tid2 = i + j;
                            const int tid3 = i + j + k;

                            if ((blockIdx.z + adjust[0])==scale[0])
                                { out1[tid3] = in1[tid2] - in2[tid2]; }

                        }
                       """)

    wavelet_filter = (1./16)*np.array([1,4,6,4,1], dtype=np.float32)    # Filter-bank for use in the a trous algorithm.
    wavelet_filter = gpuarray.to_gpu_async(wavelet_filter)

    # Initialises an empty array to store the detail coefficients.

    detail_coeffs = gpuarray.empty([scale_count-scale_adjust, in1.shape[0], in1.shape[1]], np.float32)

    # Determines whether the array is already on the GPU or not. If not, moves it to the GPU.

    try:
        gpu_in1 = gpuarray.to_gpu_async(in1.astype(np.float32))
    except:
        gpu_in1 = in1

    # Sets up some working arrays on the GPU to prevent memory transfers.

    gpu_tmp = gpuarray.empty_like(gpu_in1)
    gpu_out1 = gpuarray.empty_like(gpu_in1)
    gpu_out2 = gpuarray.empty_like(gpu_in1)

    # Sets up some parameters required by the algorithm on the GPU.

    gpu_scale = gpuarray.zeros([1], np.int32)
    gpu_adjust = gpuarray.zeros([1], np.int32)
    gpu_adjust += scale_adjust

    # Fetches the a trous kernels and sets up the unique storing kernel.

    gpu_a_trous_row_kernel, gpu_a_trous_col_kernel = gpu_a_trous()
    gpu_store_detail_coeffs = ker.get_function("gpu_store_detail_coeffs")

    grid_rows = int(in1.shape[0]//32)
    grid_cols = int(in1.shape[1]//32)

    # The following loop, which iterates up to scale_adjust, applies the a trous algorithm to the scales which are
    # considered insignificant. This is important as each set of wavelet coefficients depends on the last smoothed
    # version of the input.

    if scale_adjust>0:
        for i in range(0, scale_adjust):
            gpu_a_trous_row_kernel(gpu_in1, gpu_tmp, wavelet_filter, gpu_scale,
                                block=(32,32,1), grid=(grid_cols, grid_rows))

            gpu_a_trous_col_kernel(gpu_tmp, gpu_out1, wavelet_filter, gpu_scale,
                                block=(32,32,1), grid=(grid_cols, grid_rows))

            gpu_in1, gpu_out1 = gpu_out1, gpu_in1
            gpu_scale += 1

    # The meat of the algorithm - two sequential applications fo the a trous followed by determination and storing of
    # the detail coefficients. C0 is reassigned the value of C on each loop - C0 is always the smoothest version of the
    # input image.

    for i in range(scale_adjust, scale_count):

        gpu_a_trous_row_kernel(gpu_in1, gpu_tmp, wavelet_filter, gpu_scale,
                                block=(32,32,1), grid=(grid_cols, grid_rows))

        gpu_a_trous_col_kernel(gpu_tmp, gpu_out1, wavelet_filter, gpu_scale,
                                block=(32,32,1), grid=(grid_cols, grid_rows))           # Approximation coefficients.

        gpu_a_trous_row_kernel(gpu_out1, gpu_tmp, wavelet_filter, gpu_scale,
                                block=(32,32,1), grid=(grid_cols, grid_rows))

        gpu_a_trous_col_kernel(gpu_tmp, gpu_out2, wavelet_filter, gpu_scale,
                                block=(32,32,1), grid=(grid_cols, grid_rows))           # Approximation coefficients.

        gpu_store_detail_coeffs(gpu_in1, gpu_out2, detail_coeffs, gpu_scale, gpu_adjust,
                                block=(32,32,1), grid=(grid_cols, grid_rows, int(scale_count))) # Detail coefficients.

        gpu_in1, gpu_out1 = gpu_out1, gpu_in1
        gpu_scale += 1

    # Return values depend on mode. NOTE: store_smoothed does not work if the result stays on the gpu.

    if store_on_gpu:
        return detail_coeffs
    elif store_smoothed:
        return detail_coeffs.get(), gpu_in1.get()
    else:
        return detail_coeffs.get()