<｜begin▁of▁sentence｜>### Instruction:
Imagine you're the School Principal, diligently overseeing the intricate network that constitutes the school’s ecosystem. You’re examining the relationships and interactions between various entities within the school - departments, administrative bodies, student clubs, and so on. Each of these entities is interconnected in a complex web, much like nodes in a graph, where connections hold different significance, akin to edge weights in a graph structure.

In your role, you're tasked with mapping out these relationships to analyze and improve the dynamics of the school's network. You have compiled a comprehensive dataset that represents this graph - a dictionary where each department or entity (node) is associated with another through a nested dictionary representing those connections (edges) and their corresponding strengths (weights).

For instance, you might have something like this:

```python
{
    'Math Department': {'Science Department': 10, 'English Department': 5},
    'Science Department': {'PE Department': 8, 'Arts Department': 4},
    // And so on...
}
```

Now, as the steward of this academic landscape, you wish to employ the `to_networkx_graph` function from the NetworkX library to create a visual map of these connections. How would you proceed with such a conversion using this function, ensuring the complexity and nuances of these relationships are aptly captured within the resulting NetworkX graph? This detailed schematic would aid in identifying areas of collaboration, potential bottlenecks, or opportunities for integration between different facets of the school, ultimately enhancing the institution's operational harmony.
### Response:
import networkx as nx

# Generate a dict of dicts representation of a graph
input_data = {
  'A': {'B': 1.0, 'C': 2.0},
  'B': {'A': 1.0, 'C': 3.0},
  'C': {'A': 2.0, 'B': 3.0}
}

# Convert the dict of dicts to a NetworkX graph
G = nx.to_networkx_graph(input_data, create_using=nx.Graph)

# Print nodes and edges in the new NetworkX graph
print('Nodes:', list(G.nodes))
print('Edges:', list(G.edges(data=True)))
<|EOT|>
