input: Write a python function to accelerate a NumPy array using GPU with PyCUDA. 
  assert numpy.allclose(gpu_accelerate_array(numpy.arange(128).astype(numpy.float32)), numpy.arange(128).astype(numpy.float32) + 5)
  assert numpy.allclose(gpu_accelerate_array(numpy.zeros(128, dtype=numpy.float32)), numpy.ones(128, dtype=numpy.float32) * 5)
  assert numpy.allclose(gpu_accelerate_array(numpy.full(128, 3.0, dtype=numpy.float32)), numpy.full(128, 8.0, dtype=numpy.float32))
output: import numpy as np
  import pycuda.autoinit
  import pycuda.driver as drv
  from pycuda.compiler import SourceModule
  
  def gpu_accelerate_array(arr):
      mod = SourceModule("""
      __global__ void add_five(float *a)
      {
          int idx = threadIdx.x + blockIdx.x * blockDim.x;
          a[idx] += 5;
      }
      """)
      
      add_five = mod.get_function("add_five")
      
      arr_gpu = drv.mem_alloc(arr.nbytes)
      drv.memcpy_htod(arr_gpu, arr)
      
      block_size = 128
      grid_size = int(np.ceil(arr.size / block_size))
      
      add_five(arr_gpu, block=(block_size, 1, 1), grid=(grid_size, 1))
      
      drv.memcpy_dtoh(arr, arr_gpu)
      return arr