<｜begin▁of▁sentence｜>### Instruction:
Imagine we're crafting an innovative eco-village model in our latest green architectural project, with each building interconnected in a sustainable network. We have a digital layout representing this network, where buildings are nodes, and the pathways are edges. The current map is designed with each building's location as a fixed point within a NumPy array for precision and ease of manipulation.

Now, to enhance the scalability of our model and ensure it fits within various landscape constraints, we need to adjust every building's position proportionally. To maintain the integrity and relative spacing of our eco-village layout, how can we uniformly resize the entire network within our blueprint without individually recalibrating each location? This is, of course, while utilizing our preferred NetworkX toolkit to manage this network of interconnected building positions efficiently. 

Could you please rework this scenario into an actionable query, keeping the original meaning intact? Let us assume the data detailing the positions is conveniently organized in a dictionary associated with each node's designation, ready for us to manipulate.
### Response:
import networkx as nx
import numpy as np

# Positions dictionary with nodes as keys and positions as values
pos = {0: np.array((0, 0)), 1: np.array((1, 1)), 2: np.array((0.5, 0.5))}

# Rescale the positions using the default scale=1
rescaled_pos = nx.rescale_layout_dict(pos)

# Output the rescaled positions
print(rescaled_pos)
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