<｜begin▁of▁sentence｜>### Instruction:
Let's imagine we're at a dog training facility, and each of our furry friends represents a node in a network. Each pup has been rated on their obedience skill level on a scale that measures their performance in a continuous manner. Now, we want to visualize our pack as a connected graph, where every pooch is assigned a color that corresponds with their obedience level. The challenge here is to choose colors so that even the most subtle differences in skill are visible at a glance—like distinguishing between a beginner's sit-and-stay from a more practiced paw-shake—without resorting to a logarithmic scale that might misrepresent the progression of their training.

Our goal is to create a color spectrum for our obedience graph that is pleasing to the eye when set against the light backdrop of our training chart, and where neighboring colors are just as distinguishable as neighbors in a training class. This color mapping should use networkx, an API designed for such visualization tasks, but without applying a logarithmic transformation, which could oversimplify the nuances of our dogs' skill levels. How might we accomplish this while maintaining the integrity of our dogs' metrics?
### Response:
```python
import graspologic
from graspologic.layouts import sequential_colors

# Define a mapping of nodes to some continuous value
node_values = {'A': 0.1, 'B': 0.5, 'C': 0.8, 'D': 0.2, 'E': 0.9}

# Generate a color mapping for a light background
colors = sequential_colors(node_values, light_background=True, use_log_scale=False)

# Print the generated color mapping
for node, color in colors.items():
    print(f'Node {node} has been assigned color {color}')
```
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