<｜begin▁of▁sentence｜>### Instruction:
Imagine we're working on an environmental project where we map out the relationships between different stakeholders involved in a local conservation initiative. We have compiled our stakeholders and their connections in a format that represents an ecological network, with each node representing a stakeholder and each edge signifying a connection between them. The data is structured as such:

```
{
  'directed': False,
  'multigraph': False,
  'graph': [],
  'nodes': [{'id': 1}, {'id': 2}, {'id': 3}],
  'adjacency': [
    [{'id': 2}],
    [{'id': 1}, {'id': 3}],
    [{'id': 2}]
  ]
}
```

Our objective is to bring this data to life using a tool that allows us to visualize and further analyze the network's structure and dynamics. We've chosen NetworkX for this purpose, as it's well-suited for the complex networks often found in ecosystems and social interactions related to environmental conservation.

Thus, the practical question we need to address is: Using the `adjacency_graph` function from NetworkX, how would we reconstruct our network from this data to better facilitate our environmental advocacy efforts and understand the interconnectedness of the actors in our ecological landscape?
### Response:
import networkx as nx
from networkx.readwrite import json_graph

# Create a graph
G = nx.Graph()
G.add_edge(1, 2)
G.add_edge(2, 3)

# Generate adjacency data format from graph
adjacency_data_format = json_graph.adjacency_data(G)

# Create a new graph from the adjacency data format
H = json_graph.adjacency_graph(adjacency_data_format)

print('Nodes of restored graph:', H.nodes())
print('Edges of restored graph:', H.edges())
<|EOT|>
