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        }
    }, $nodes);
    print("Total params: $total_params\n");
    print('_' x $line_length, "\n");
}

=head2 plot_network

    convert symbol to dot object for visualization

    Parameters
    ----------
    title: str
        title of the dot graph
    symbol: AI::MXNet::Symbol
        symbol to be visualized
    shape: HashRef[Shape]
        If supplied, the visualization will include the shape
        of each tensor on the edges between nodes.
    node_attrs: HashRef of node's attributes
        for example:
            {shape => "oval",fixedsize => "false"}
            means to plot the network in "oval"
    hide_weights: Bool
        if True (default) then inputs with names like `*_weight`
        or `*_bias` will be hidden

    Returns
    ------
    dot: Diagraph
        dot object of symbol
=cut


method plot_network(
    AI::MXNet::Symbol       $symbol,
    Str                    :$title='plot',
    Str                    :$save_format='ps',
    Maybe[HashRef[Shape]]  :$shape=,
    HashRef[Str]           :$node_attrs={},
    Bool                   :$hide_weights=1
)
{
    eval { require GraphViz; };
    Carp::confess("plot_network requires GraphViz module") if $@;
    my $draw_shape;
    my %shape_dict;
    if(defined $shape)
    {
        $draw_shape = 1;
        my $interals = $symbol->get_internals;
        my (undef, $out_shapes, undef) = $interals->infer_shape(%{ $shape });
        Carp::confess("Input shape is incomplete")
            unless defined $out_shapes;
        @shape_dict{ @{ $interals->list_outputs } } = @{ $out_shapes };
    }
    my $conf = decode_json($symbol->tojson);
    my $nodes = $conf->{nodes};
    my %node_attr = (
        qw/ shape box fixedsize true
            width 1.3 height 0.8034 style filled/,
        %{ $node_attrs }
    );
    my $dot = AI::MXNet::Visualization::PythonGraphviz->new(
        graph  => GraphViz->new(name => $title),
        format => $save_format
    );
    # color map
    my @cm = (
        "#8dd3c7", "#fb8072", "#ffffb3", "#bebada", "#80b1d3",
        "#fdb462", "#b3de69", "#fccde5"
    );
    # make nodes
    my %hidden_nodes;
    for my $node (@{ $nodes })
    {
        my $op   = $node->{op};
        my $name = $node->{name};
        # input data
        my %attr = %node_attr;
        my $label = $name;
        if($op eq 'null')
        {
            if($name =~ /(?:_weight|_bias|_beta|_gamma|_moving_var|_moving_mean)$/)
            {
                if($hide_weights)
                {
                    $hidden_nodes{$name} = 1;
                }
                # else we don't render a node, but
                # don't add it to the hidden_nodes set
                # so it gets rendered as an empty oval
                next;
            }
            $attr{shape} = 'ellipse'; # inputs get their own shape
            $label = $name;
            $attr{fillcolor} = $cm[0];
        }
        elsif($op eq 'Convolution')
        {
            my @k = $node->{attr}{kernel} =~ /(\d+)/g;
            my @stride = ($node->{attr}{stride}//'') =~ /(\d+)/g;
            $stride[0] //= 1;
            $label = "Convolution\n".join('x',@k).'/'.join('x',@stride).", $node->{attr}{num_filter}";
            $attr{fillcolor} = $cm[1];
        }
        elsif($op eq 'FullyConnected')
        {
            $label = "FullyConnected\n$node->{attr}{num_hidden}";
            $attr{fillcolor} = $cm[1];
        }
        elsif($op eq 'BatchNorm')
        {
            $attr{fillcolor} = $cm[3];
        }
        elsif($op eq 'Activation' or $op eq 'LeakyReLU')
        {
            $label = "$op\n$node->{attr}{act_type}";
            $attr{fillcolor} = $cm[2];
        }
        elsif($op eq 'Pooling')