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Numerical key cache storage for efficient data management in AI art projects.
The CacheBackendDataNumberKey __Inspire
node is designed to store data in a cache using a numerical key. This node is particularly useful for managing and retrieving data efficiently in scenarios where numerical identifiers are preferred or required. By caching data, you can significantly reduce the time and computational resources needed to repeatedly access or compute the same information. This node is part of the InspirePack Backend, which aims to streamline backend operations for AI art projects, ensuring that data is readily available and easily manageable.
The key
parameter is an integer that serves as the unique identifier for the data you wish to cache. This key must be within the range of 0 to 0xffffffffffffffff (a very large number), ensuring a wide range of possible keys. The default value is 0. This parameter is crucial as it determines how the data is stored and retrieved from the cache.
The tag
parameter is a string that provides a short description or label for the cached data. This helps in identifying and managing the cached data more effectively. The tag is not multiline and serves as a placeholder for a brief description, making it easier to understand the context or purpose of the cached data.
The data
parameter can be of any type and represents the actual data you wish to cache. This could be anything from model parameters to image data, depending on your specific use case. The flexibility of this parameter allows you to cache a wide variety of data types, making the node highly versatile.
The data opt
output parameter returns the data that was cached. This ensures that the data is not only stored but also immediately available for further use or processing. The output is of the same type as the input data, maintaining consistency and ease of use.
key
parameter is unique for each distinct piece of data you wish to cache to avoid overwriting existing data.tag
values to make it easier to identify and manage your cached data, especially when dealing with large datasets.<key>
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