How to use the pyamg.gallery function in pyamg

for x in range(region_size[1]):
            if img_mask[y,x]:
                index = x+y*region_size[1]
                A[index, index] = 4
                if index+1 < np.prod(region_size):
                    A[index, index+1] = -1
                if index-1 >= 0:
                    A[index, index-1] = -1
                if index+region_size[1] < np.prod(region_size):
                    A[index, index+region_size[1]] = -1
                if index-region_size[1] >= 0:
                    A[index, index-region_size[1]] = -1
    A = A.tocsr()

    # create poisson matrix for b
    P = pyamg.gallery.poisson(img_mask.shape)

    # for each layer (ex. RGB)
    for num_layer in range(img_target.shape[0]):
        # get subimages
        t = img_target[num_layer, region_target[0]:region_target[2],region_target[1]:region_target[3]]
        s = img_source[num_layer, region_source[0]:region_source[2], region_source[1]:region_source[3]]
        t = t.flatten()
        s = s.flatten()

        # create b
        b = P * s
        for y in range(region_size[0]):
            for x in range(region_size[1]):
                if not img_mask[y,x]:
                    index = x+y*region_size[1]
                    b[index] = t[index]

for x in range(region_size[1]):
            if img_mask[y, x]:
                index = x + y * region_size[1]
                A[index, index] = 4
                if index + 1 < np.prod(region_size):
                    A[index, index + 1] = -1
                if index - 1 >= 0:
                    A[index, index - 1] = -1
                if index + region_size[1] < np.prod(region_size):
                    A[index, index + region_size[1]] = -1
                if index - region_size[1] >= 0:
                    A[index, index - region_size[1]] = -1
    A = A.tocsr()

    # create poisson matrix for b
    P = pyamg.gallery.poisson(img_mask.shape)

    # for each layer (ex. RGB)
    for num_layer in range(img_target.shape[2]):
        # get subimages
        t = img_target[region_target[0]:region_target[2], region_target[1]:region_target[3], num_layer]
        s = img_source[region_source[0]:region_source[2], region_source[1]:region_source[3], num_layer]
        t = t.flatten()
        s = s.flatten()

        # create b
        b = P * s
        for y in range(region_size[0]):
            for x in range(region_size[1]):
                if not img_mask[y, x]:
                    index = x + y * region_size[1]
                    b[index] = t[index]

for x in range(region_size[1]):
      if img_mask[y, x]:
        index = x + y * region_size[1]
        coff_mat[index, index] = 4
        if index + 1 < np.prod(region_size):
          coff_mat[index, index + 1] = -1
        if index - 1 >= 0:
          coff_mat[index, index - 1] = -1
        if index + region_size[1] < np.prod(region_size):
          coff_mat[index, index + region_size[1]] = -1
        if index - region_size[1] >= 0:
          coff_mat[index, index - region_size[1]] = -1
  coff_mat = coff_mat.tocsr()

  # create poisson matrix for b
  poisson_mat = pyamg.gallery.poisson(img_mask.shape)
  # for each layer (ex. RGB)
  for num_layer in range(img_target.shape[2]):
    # get subimages
    t = img_target[region_target[0]:region_target[2],
                   region_target[1]:region_target[3], num_layer]
    s = img_source[region_source[0]:region_source[2],
                   region_source[1]:region_source[3], num_layer]
    t = t.flatten()
    s = s.flatten()

    # create b
    b = poisson_mat * s
    for y in range(region_size[0]):
      for x in range(region_size[1]):
        if not img_mask[y, x]:
          index = x + y * region_size[1]