<｜begin▁of▁sentence｜>### Instruction:
As a professional in network analysis, I often work with datasets containing information such as names of individuals in a social network or a contact list. In this scenario, I receive a dataset containing names of individuals and I need to manage unique identifiers for each name. This involves tasks such as generating unique IDs using the UniqueIdGenerator class in the networkx library, checking if a name already has an ID, updating the ID for a specific name, counting the total number of names with IDs, and keeping track of the direct and reverse mappings of names to IDs. This allows me to efficiently analyze and manipulate the network data without losing track of individual identities.
### Response:
from igraph import UniqueIdGenerator

# Initialize a unique ID generator
id_gen = UniqueIdGenerator()

# Add some names and retrieve their IDs
id_alice = id_gen['Alice']
print('Alice ID:', id_alice)
id_bob = id_gen['Bob']
print('Bob ID:', id_bob)

# Check if a name has an ID
print('Has Alice an ID?', 'Alice' in id_gen)
print('Has Cindy an ID?', 'Cindy' in id_gen)

# Override ID for a specific name
id_gen['Alice'] = 10
print('New Alice ID:', id_gen['Alice'])

# Get the number of items with IDs
print('Number of names with IDs:', len(id_gen))

# Access the reverse mapping
reverse_mapping = id_gen.reverse_dict()
print('Reverse mapping:', reverse_mapping)

# Retrieve all values
values = id_gen.values()
print('Values:', list(values))
<|EOT|>
