<｜begin▁of▁sentence｜>### Instruction:
As a software architect designing the high-level structure of software systems and applications, imagine you're tasked with optimizing a transportation network model for a smart city project. You have a graph representing road connections between key locations, with each edge having an associated attribute denoting its distance in kilometers. Let's say you have a graph with the following road connections: [(0, 1), (1, 2), (2, 3)]. The distances for these road segments are 1.5 km, 2.5 km, and 3.5 km, respectively.

Now, you need to use different methods from the EdgeSeq API to manipulate and access these distance attributes efficiently within your transportation network model. How would you approach this task, considering the specific needs of your smart city project?
### Response:
from igraph import Graph
import igraph as ig

# Create a graph
g = ig.Graph(edges=[(0, 1), (1, 2), (2, 3)])

# Adding attribute 'weight' to all edges
g.es['weight'] = [1.5, 2.5, 3.5]

# Get edge attributes' names
attribute_names = g.es.attribute_names()
print('Edge Attributes:', attribute_names)

# Get values of the 'weight' attribute for all edges
weights = g.es.get_attribute_values('weight')
print('Weights:', weights)

# Check if the EdgeSeq contains all edges exactly once
is_all_edges = g.es.is_all()
print('Contains all edges:', is_all_edges)

# Set a new value for all edges attribute 'weight'
g.es.set_attribute_values('weight', [4.5, 5.5, 6.5])
updated_weights = g.es.get_attribute_values('weight')
print('Updated weights:', updated_weights)
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