In some scenarios, you may want to reuse input/output tensors. This often happens when you want to chain 2 models (ie. feed one's output as input to another), or want to accelerate inference speed during multiple inference runs.
### Chaining: Feed model A's output(s) as input(s) to model B
```cs
InferenceSession session1, session2; // let's say 2 sessions are initialized
Tensor<float> t1; // let's say data is fed into the Tensor objects
var inputs2 = new List<NamedOnnxValue>() { input2 };
// session2 inference
using (var results = session2.Run(inputs2))
{
// manipulate the results
}
}
```
### Multiple inference runs with fixed sized input(s) and output(s)
If the model have fixed sized inputs and outputs of numeric tensors, you can use "FixedBufferOnnxValue" to accelerate the inference speed. By using "FixedBufferOnnxValue", the container objects only need to be allocated/disposed one time during multiple InferenceSession.Run() calls. This avoids some overhead which may be beneficial for smaller models where the time is noticeable in the overall running time.
An example can be found at `TestReusingFixedBufferOnnxValueNonStringTypeMultiInferences()`:
