<｜begin▁of▁sentence｜>### Instruction:
Imagine you're working on a developmental project as a surveyor, and you've been handed a digital representation of a plot of land with various reference points. These points have been meticulously mapped out in a graph, with each node corresponding to a specific geographic marker. The data for these points is stored in a dictionary format, where each key represents a unique node identifier, with the associated value being a numpy array that details the node's coordinates on the survey grid.

Your task is to adjust the scale of this graph to facilitate a more detailed examination of the area. To be precise, you need to double the current scale of the layout to enhance the visualization of the space between each node. This rescaling process would involve using the capabilities of software like NetworkX, specifically the rescale_layout_dict function. Could you please guide how this rescaling can be accomplished while retaining the accurate relative positioning of the nodes? It's essential to enlarge the node layout by a multiplication factor of 2.
### Response:
import networkx as nx
import numpy as np

# Coordinates for nodes in a dictionary
pos = {0: np.array((0, 0)), 1: np.array((1, 1)), 2: np.array((0.5, 0.5))}

# Rescale the positions
scaled_pos = nx.rescale_layout_dict(pos, scale=2)

# Output the rescaled positions
print(scaled_pos)
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