<｜begin▁of▁sentence｜>### Instruction:
In the realm of political analysis, let's consider a situation where we are attempting to map out the network of influences among a group of five key players. Our preliminary data suggests that one individual is connected to three others, two individuals maintain connections with two others, and one person is linked with a single individual.

To ensure that our model accurately reflects potential real-world structures, we must examine the viability of this connectivity pattern. Envision this as a political relationship graph, where each player is a node, and their connections represent direct influence or communication lines.

Our task is to ascertain whether this degree sequence of [3, 2, 2, 2, 1], representing the number of connections for each player, can be interpreted as a valid configuration in a straightforward political influence graph—where no self-connections or multiple connections between the same individuals are present.

For such an analysis, we may employ the analytical tool NetworkX, specifically the function "is_graphical", which can verify whether a given degree sequence can manifest in a simple graph.

To retain the connection with actual data handling processes and file referencing, if this were to be confirmed using a dataset contained within a file named 'political_network.gml', we would duly note the file name when discussing the applicability of the degree sequence to our model. However, since the question does not request the actual application or guide on using the function but instead focuses on interpreting the possibility of such network representation, the 'political_network.gml' file reference assists in illustrating the kind of data we might be dealing with.
### Response:
import networkx as nx

# Given degree sequence
sequence = [3, 2, 2, 2, 1]

# Check if the sequence is graphical using default method (Erdős-Gallai)
print(nx.is_graphical(sequence))

# Check using Havel-Hakimi algorithm
print(nx.is_graphical(sequence, method='hh'))
<|EOT|>
