Following this instruction :
1. Strictly adhere to PEP-8 standards, ensuring code is clean, well-structured, and follows Python's style guide for readability.
2. Use appropriate modules like `typing` for type hints, ensuring code clarity, maintainability, and leveraging Python's standard libraries effectively.
3. Focus on time, space, robustness, scalability; write optimized, efficient code that can handle large datasets and scale seamlessly.
4. Implement comprehensive error handling, catching exceptions gracefully, providing meaningful error messages, and ensuring code robustness under unexpected conditions.
5. Ensure the developer agent writes advanced, maintainable Python code, balancing performance and readability while adhering to best practices.





in below function darwback 1) beautification 2) title , 3) xais details, plotly not looking professionaly 



def visualize_attributions(
    attributions: Dict[str, torch.Tensor], input_text: str, top_k: int, max_layers: int = 20
) -> None:
    """
    Visualize neuron attributions using Plotly.

    Args:
        attributions (Dict[str, torch.Tensor]): Neuron attributions per layer.
        input_text (str): The input string.
        top_k (int): Number of top neurons visualized per layer.
        max_layers (int): Maximum number of layers to visualize.
    """
    try:
        layers = list(attributions.keys())
        num_layers = len(layers)

        if num_layers == 0:
            print("No attributions to visualize.", file=sys.stderr)
            return

        # Limit the number of layers to visualize
        if num_layers > max_layers:
            print(
                f"Number of layers ({num_layers}) exceeds max_layers ({max_layers}). "
                f"Only the first {max_layers} layers will be visualized.",
                file=sys.stderr,
            )
            layers = layers[:max_layers]
            num_layers = max_layers

        # Calculate appropriate vertical_spacing
        vertical_spacing = min(0.05, 1 / (num_layers - 1)) if num_layers > 1 else 0

        fig = make_subplots(
            rows=num_layers,
            cols=1,
            subplot_titles=layers,
            vertical_spacing=vertical_spacing,
        )

        for idx, layer in enumerate(layers, start=1):
            data = attributions[layer].numpy()
            neurons = list(range(len(data)))
            fig.add_trace(
                go.Bar(
                    x=neurons,
                    y=data,
                    marker=dict(color=data, colorscale="Viridis"),
                    showlegend=False,
                ),
                row=idx,
                col=1,
            )

            fig.update_yaxes(title_text="Attribution", row=idx, col=1)
            fig.update_xaxes(title_text="Neuron Index", row=idx, col=1)

        # Adjust the figure height based on the number of layers
        fig.update_layout(
            title=f"Top {top_k} Neuron Attributions for Input: \"{input_text}\"",
            height=1000 * num_layers if num_layers > 0 else 600,
            showlegend=False,
            hovermode="closest",
        )
        fig.show()
    except Exception as e:
        print(f"Error during visualization: {e}", file=sys.stderr)
        raise