normalize.swift

        // Retrieve results
        guard let outputFeatures = featureProvider.featureValue(for: selectedModel.outputLayerName)?.multiArrayValue else {
            fatalError("Couldn't retrieve features")
        }
        
        // Calculate total buffer size by multiplying shape tensor's dimensions
        let bufferSize = outputFeatures.shape.lazy.map { $0.intValue }.reduce(1, { $0 * $1 })
        
        // Get data pointer to the buffer
        let dataPointer = UnsafeMutableBufferPointer(start: outputFeatures.dataPointer.assumingMemoryBound(to: Double.self),
                                                     count: bufferSize)
        
        // Prepare buffer for single-channel image result
        var imgData = [UInt8](repeating: 0, count: bufferSize)
        
        // Normalize result features by applying sigmoid to every pixel and convert to UInt8
        for i in 0..<inputW {
            for j in 0..<inputH {
                let idx = i * inputW + j
                let value = dataPointer[idx]
                
                let sigmoid = { (input: Double) -> Double in
                    return 1 / (1 + exp(-input))
                }
                
                let result = sigmoid(value)
                imgData[idx] = UInt8(result * 255)
            }
        }