### Load and Run ONNX Network in Go

Source: https://github.com/yalue/onnxruntime_go/blob/master/README.md

Illustrates loading an ONNX network, creating input and output tensors, and running inference. Error handling is omitted for brevity. Explicitly set the shared library path for stability.

```go
import (
    "fmt"
    ort "github.com/yalue/onnxruntime_go"
    "os"
)

func main() {
    // This line _may_ be optional; by default the library will try to load
    // "onnxruntime.dll" on Windows, and "onnxruntime.so" on any other system.
    // For stability, programs should always set this explicitly.
    ort.SetSharedLibraryPath("path/to/onnxruntime.so")

    err := ort.InitializeEnvironment()
    if err != nil {
        panic(err)
    }
    defer ort.DestroyEnvironment()

    // For a slight performance boost and convenience when re-using existing
    // tensors, this library expects the user to create all input and output
    // tensors prior to creating the session. If this isn't ideal for your use
    // case, see the DynamicAdvancedSession type in the documnentation, which
    // allows input and output tensors to be specified when calling Run()
    // rather than when initializing a session.
    inputData := []float32{0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}
    inputShape := ort.NewShape(2, 5)
    inputTensor, err := ort.NewTensor(inputShape, inputData)
    defer inputTensor.Destroy()
    // This hypothetical network maps a 2x5 input -> 2x3x4 output.
    outputShape := ort.NewShape(2, 3, 4)
    outputTensor, err := ort.NewEmptyTensor[float32](outputShape)
    defer outputTensor.Destroy()

    session, err := ort.NewAdvancedSession("path/to/network.onnx",
        []string{"Input 1 Name"}, []string{"Output 1 Name"},
        []ort.Value{inputTensor}, []ort.Value{outputTensor}, nil)
    defer session.Destroy()

    // Calling Run() will run the network, reading the current contents of the
    // input tensors and modifying the contents of the output tensors.
    err = session.Run()

    // Get a slice view of the output tensor's data.
    outputData := outputTensor.GetData()

    // If you want to run the network on a different input, all you need to do
    // is modify the input tensor data (available via inputTensor.GetData())
    // and call Run() again.

    // ...
}
```

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