<｜begin▁of▁sentence｜>### Instruction:
All right, envision yourself at the helm of an inspiring workshop, where the minds gathered around are eager to construct and navigate the intricate pathways of personal connections, not unlike the nodes and edges of a life-enriching graph. We have a vivid map laid out in front of us, a graph denote the richness of relationships—nodes labeled as 'Node1', 'Node2', and 'Node3', a reflection of individual selves, and the edges, akin to the bridges we build between each other, carry the designations 'Edge1-2', 'Edge2-3', and 'Edge3-1', symbolizing our interactions and bonds.

In our hands, we possess this tool, akin to a GML file for life's grand design, and through it, we aim to learn how we might harness the edges API within the networkx library to illuminate all the connections with their respective labels. It's a journey not just to understand the script of connections but to read aloud the names of the ties that bind us. This question is not a mere technical query but a challenge to delve into the relationships and make visible the labels that define them. How can we leverage networkx to not only see the edges but to read the story they tell about our network's synergy?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()

# Add nodes to the graph
G.add_nodes_from([
    (1, {'label': 'Node1'}),
    (2, {'label': 'Node2'}),
    (3, {'label': 'Node3'})
])

# Add edges to the graph
G.add_edges_from([
    (1, 2, {'label': 'Edge1-2'}),
    (2, 3, {'label': 'Edge2-3'}),
    (3, 1, {'label': 'Edge3-1'})
])

# Use the edges API to get the edges of the graph
edges_view = G.edges(data=True)

# Print out all the edges with their associated data
for edge in edges_view:
    print(f"Edge from {edge[0]} to {edge[1]} with label: {edge[2]['label']}")
<|EOT|>
