How to use the tensorly.to_numpy function in tensorly

fig = plt.figure()

for i, pattern in enumerate(patterns):

    print('fitting pattern n.{}'.format(i))

    # Generate the original image
    weight_img = gen_image(region=pattern, image_height=image_height, image_width=image_width)
    weight_img = tl.tensor(weight_img)

    # Generate the labels
    y = tl.dot(partial_tensor_to_vec(X, skip_begin=1), tensor_to_vec(weight_img))

    # Plot the original weights
    ax = fig.add_subplot(n_rows, n_columns, i*n_columns + 1)
    ax.imshow(tl.to_numpy(weight_img), cmap=plt.cm.OrRd, interpolation='nearest')
    ax.set_axis_off()
    if i == 0:
        ax.set_title('Original\nweights')

    for j, rank in enumerate(ranks):
        print('fitting for rank = {}'.format(rank))

        # Create a tensor Regressor estimator
        estimator = TuckerRegressor(weight_ranks=[rank, rank], tol=10e-7, n_iter_max=100, reg_W=1, verbose=0)

        # Fit the estimator to the data
        estimator.fit(X, y)

        ax = fig.add_subplot(n_rows, n_columns, i*n_columns + j + 2)
        ax.imshow(tl.to_numpy(estimator.weight_tensor_), cmap=plt.cm.OrRd, interpolation='nearest')
        ax.set_axis_off()

ax = fig.add_subplot(n_rows, n_columns, i*n_columns + 1)
    ax.imshow(tl.to_numpy(weight_img), cmap=plt.cm.OrRd, interpolation='nearest')
    ax.set_axis_off()
    if i == 0:
        ax.set_title('Original\nweights')

    for j, rank in enumerate(ranks):

        # Create a tensor Regressor estimator
        estimator = KruskalRegressor(weight_rank=rank, tol=10e-7, n_iter_max=100, reg_W=1, verbose=0)

        # Fit the estimator to the data
        estimator.fit(X, y)

        ax = fig.add_subplot(n_rows, n_columns, i*n_columns + j + 2)
        ax.imshow(tl.to_numpy(estimator.weight_tensor_), cmap=plt.cm.OrRd, interpolation='nearest')
        ax.set_axis_off()

        if i == 0:
            ax.set_title('Learned\nrank = {}'.format(rank))

plt.suptitle("Kruskal tensor regression")
plt.show()

def to_image(tensor):
    """A convenience function to convert from a float dtype back to uint8"""
    im = tl.to_numpy(tensor).squeeze()
    im -= im.min()
    im /= im.max()
    im *= 255
    return im.astype(np.uint8)

# Subtract the projection from A_new:  b <- b - a * projection
        A_new = A_new - A_new * tl.reshape(projection, (tl.shape(A_new)[0], 1))

        # Delete the selected row
        mask.pop(max_row_idx)
        A_new = A_new[mask,:]

        # update the row_idx and rest_of_rows
        row_idx[i] = rest_of_rows[max_row_idx]
        rest_of_rows = rest_of_rows[mask]
        i = i + 1

    row_idx = tl.tensor(row_idx, dtype=tl.int64)
    inverse = tl.solve(A[row_idx,:], tl.eye(tl.shape(A[row_idx,:])[0]))
    row_idx = tl.to_numpy(row_idx)

    return row_idx, inverse