<｜begin▁of▁sentence｜>### Instruction:
Let's create a scenario where we, as robotics engineers, are conceptualizing an advanced robot that requires a complex internal signaling network to function efficiently. We're utilizing NetworkX for simulating and analyzing this intricate network of signals. We possess a text file, let's call it "robot_network.adjlist", which contains an adjacency list detailing the connections within the robot's signaling system. In this file, each signal node may have an associated 'name' and an 'weight' to signify its identifier and importance, respectively.

To accurately model our robot's signaling network with NetworkX, could you walk me through the process of translating the contents of "robot_network.adjlist" into a graph structure using NetworkX's `parse_multiline_adjlist` function? Additionally, I'm interested in figuring out how to compile a list of all the signal nodes along with their corresponding identifiers and importance factors from this graph. We need such a list to ensure that we understand the hierarchy of signals within our robot and can optimize the network for maximum efficiency.
### Response:
import networkx as nx

# Input data in multiline adjlist format
lines = [
    '1 2',
    "2 {'weight':3, 'name': 'Frodo'}",
    '3 {}',
    '2 1',
    "5 {'weight':6, 'name': 'Saruman'}",
]

# Parse lines to create a graph
G = nx.parse_multiline_adjlist(iter(lines), nodetype=int)

# Output the nodes and their attributes
elements = [(node, G.nodes[node].get('name', 'Unknown'), G.nodes[node].get('weight', None)) for node in G]
print(elements)
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