In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

[Q]: The library design of Torch-Struct follows the distributions API used by both TensorFlow and PyTorch BIBREF29. 
 Question: Does API provide ability to connect to models written in some other deep learning framework?
[A]: Yes


[Q]: The ratio of correct `translations' (matches) was used as an evaluation measure. 
 Question: What evaluation metric do they use?
[A]: Accuracy


[Q]: Drawing on the concept of variance in mathematics, local variance loss is defined as the reciprocal of its variance expecting the attention model to be able to focus on more salient parts. The standard variance calculation is based on the mean of the distribution. However, as previous work BIBREF15, BIBREF16 mentioned that the median value is more robust to outliers than the mean value, we use the median value to calculate the variance of the attention distribution. Thus, local variance loss can be calculated as:

where $\hat{\cdot }$ is a median operator and $\epsilon $ is utilized to avoid zero in the denominator. 
 Question: How do they define local variance?
[A]:
The reciprocal of the variance of the attention distribution