human
Complete the below
def main():

    ''''Entry point of script.'
    '''
 Can you provide a the Python program you requested?
assistant
#Here is the Python program you requested.
    args = _parse_args()
    model = serial.load(args.input)
    if (not isinstance(model, MLP)):
        print ('Expected    the    .pkl    file    to    contain    an    MLP,    got    a    %s.' % str(model.type))
        sys.exit(1)
    def get_figure_and_axes(conv_layers, window_width=800):
        kernel_display_width = 20
        margin = 5
        grid_square_width = (kernel_display_width + margin)
        num_columns = (window_width // grid_square_width)
        max_num_channels = numpy.max([layer.get_input_space().num_channels for layer in conv_layers])
        num_rows = (max_num_channels // num_columns)
        if ((num_rows * num_columns) < max_num_channels):
            num_rows += 1
        assert ((num_rows * num_columns) >= max_num_channels)
        window_width = 15
        window_height = (window_width * ((num_rows * 1.8) / num_columns))
        (figure, all_axes) = pyplot.subplots(num_rows, num_columns, squeeze=False, figsize=(window_width, window_height))
        for (unit_index, axes) in enumerate(all_axes.flat):
            subplot_title = axes.set_title(('%d' % unit_index))
            subplot_title.set_size(8)
            subplot_title.set_color((0.3, 0.3, 0.3))
        for axes_row in all_axes:
            for axes in axes_row:
                axes.get_xaxis().set_visible(False)
                axes.get_yaxis().set_visible(False)
        return (figure, all_axes)
    conv_layers = _get_conv_layers(model)
    (figure, all_axes) = get_figure_and_axes(conv_layers)
    title_text = figure.suptitle('title')
    pyplot.tight_layout(h_pad=0.1, w_pad=0.5)
    layer_index = numpy.array(0)
    unit_indices = numpy.zeros(len(model.layers), dtype=int)
    def redraw():
        '\n                                Draws    the    currently    selected    convolutional    kernel.\n                                '
        axes_list = all_axes.flatten()
        layer = conv_layers[layer_index]
        unit_index = unit_indices[layer_index, ...]
        weights =...