This is an [Azure Function](https://azure.microsoft.com/services/functions/) example that uses ORT with C# for inference on an NLP model created with SciKit Learn.
log.LogInformation("C# HTTP trigger function processed a request.");
string review = req.Query["review"];
string requestBody = await new StreamReader(req.Body).ReadToEndAsync();
dynamic data = JsonConvert.DeserializeObject(requestBody);
review = review ?? data?.review;
// Get path to model to create inference session.
var modelPath = "./model.onnx";
// create input tensor (nlp example)
var inputTensor = new DenseTensor<string>(new string[] { review }, new int[] { 1, 1 });
// Create input data for session.
var input = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor<string>("input", inputTensor) };
// Create an InferenceSession from the Model Path.
var session = new InferenceSession(modelPath);
// Run session and send input data in to get inference output. Call ToList then get the Last item. Then use the AsEnumerable extension method to return the Value result as an Enumerable of NamedOnnxValue.
var output = session.Run(input).ToList().Last().AsEnumerable<NamedOnnxValue>();
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
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()`:
