code: public static int cudnnBatchNormalizationForwardInference(
          cudnnHandle handle, 
          int mode, 
          Pointer alpha, /** alpha[0] = result blend factor */
          Pointer beta, /** beta[0] = dest layer blend factor */
          cudnnTensorDescriptor xDesc, 
          Pointer x, /** NxCxHxW */
          cudnnTensorDescriptor yDesc, 
          Pointer y, /** NxCxHxW */
          cudnnTensorDescriptor bnScaleBiasMeanVarDesc, 
          Pointer bnScale, 
          Pointer bnBias, 
          Pointer estimatedMean, 
          Pointer estimatedVariance, 
          double epsilon)
      {
          return checkResult(cudnnBatchNormalizationForwardInferenceNative(handle, mode, alpha, beta, xDesc, x, yDesc, y, bnScaleBiasMeanVarDesc, bnScale, bnBias, estimatedMean, estimatedVariance, epsilon));
      }
docs: <pre>
  Performs Batch Normalization during Inference:
  y[i] = bnScale[k]*(x[i]-estimatedMean[k])/sqrt(epsilon+estimatedVariance[k]) + bnBias[k]
  with bnScale, bnBias, runningMean, runningInvVariance tensors indexed
  according to spatial or per-activation mode. Refer to cudnnBatchNormalizationForwardTraining
  above for notes on function arguments.
  </pre>