<｜begin▁of▁sentence｜>### Instruction:
Imagine you're crafting an intricate digital masterpiece, a visual network where every brushstroke represents a connection between your creative elements. This network isn't just a splash of color - each link carries a specific weight, conveying the depth of the relationship between each pair of elements.

Now, let's dive into the virtual landscape you're mapping out. You have a sophisticated palette, a dictionary of dictionaries, where each primary hue (the top-level key) stands for an element (node) on your canvas. The shades blended into each primary hue (the sub-dictionary) are the other elements it's connected to, and the intensity of each mix (the edge attributes) highlights the strength of their bond—perhaps through varying weight.

As an adept multimedia artist, how would you utilize the `from_dict_of_dicts` feature of NetworkX to translate this complex dictionary into a structured graphical form? Could you paint a scenario for us where you bring this method into play? Consider this as our palette:

```
{
    "Color Red": {"Color Blue": {"weight": 2}, "Color Yellow": {"weight": 3}},
    "Color Blue": {"Color Red": {"weight": 2}, "Color Green": {"weight": 4}},
    "Color Yellow": {"Color Red": {"weight": 3}},
    "Color Green": {"Color Blue": {"weight": 4}}
}
```

In this scenario, the colors are the nodes, and their connections, with associated weights, represent the edges. How would you demonstrate using the `from_dict_of_dicts` function to convert this collection of hues and shades into a coherent NetworkX graph, ready to be displayed in your next digital gallery?
### Response:
import networkx as nx

# Dictionary of dictionaries representation
# Each key in the main dictionary represents a node.
# The value for each key is a dictionary where the
# keys are nodes it is connected to, and the values are the edge attributes.
dod = {0: {1: {'weight': 1}, 2: {'weight': 2}}, 1: {0: {'weight': 1}, 2: {'weight': 3}}, 2: {0: {'weight': 2}, 1: {'weight': 3}}}

# Create a graph from the dictionary of dictionaries
G = nx.from_dict_of_dicts(dod)

# Print the edges with attributes
print(G.edges(data=True))
<|EOT|>
