<｜begin▁of▁sentence｜>### Instruction:
Imagine you are in a clinical laboratory, analyzing a series of samples that have been codified into two distinct sets: one set is arranged in a sequential lineup much like test tubes lined up for systematic examination (resembling a linear path of five nodes), and the other is a pair of separate dyads of samples (corresponding to two pairs of connected nodes, 1-2 and 3-4, yet overall not interconnected). To further understand the relationships between the samples, compute the mean length of steps it would take to go from one point to the other in the sequential set, and also determine this average value within each of the two paired sets. 

You have been provided with data which is akin to a pattern of how certain diseases progress and transmit from one stage to the next in a linear fashion for the first set, and in an isolated cluster manner for the second. These patterns are akin to network structures, and our goal is to utilize software akin to NetworkX to calculate the mean shortest path length for the sequential sample line and for the individual paired dyads, shedding light on the progression dynamics of the pathology under investigation. 

While handling this task, consider the data is coded into a GML file named "sample_network_paths.gml", which you would typically load into an appropriate analysis tool. This practical data analysis serves to produce valuable insights that might contribute to the efficient diagnosis and treatment plans for patients, elucidating the intricacies of disease pathways.
### Response:
import networkx as nx

# Create a path graph with 5 nodes
G = nx.path_graph(5)

# Calculate the average shortest path length
asp_length = nx.average_shortest_path_length(G)

print('Average shortest path length:', asp_length)

# Handle disconnected graphs - compute average shortest path length for each component
G_disconnected = nx.Graph([(1, 2), (3, 4)])
for C in (G_disconnected.subgraph(c).copy() for c in nx.connected_components(G_disconnected)):
    print('Average shortest path length in component:', nx.average_shortest_path_length(C))
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