code: public static boolean checkGradients(ComputationGraph graph, double epsilon, double maxRelError,
                      double minAbsoluteError, boolean print, boolean exitOnFirstError, INDArray[] inputs,
                      INDArray[] labels) {
          return checkGradients(graph, epsilon, maxRelError, minAbsoluteError, print, exitOnFirstError, inputs, labels, null, null, null);
      }
docs: Check backprop gradients for a ComputationGraph
  @param graph ComputationGraph to test. This must be initialized.
  @param epsilon Usually on the order of 1e-4 or so.
  @param maxRelError Maximum relative error. Usually < 0.01, though maybe more for deep networks
  @param minAbsoluteError Minimum absolute error to cause a failure. Numerical gradients can be non-zero due to precision issues.
  For example, 0.0 vs. 1e-18: relative error is 1.0, but not really a failure
  @param print Whether to print full pass/failure details for each parameter gradient
  @param exitOnFirstError If true: return upon first failure. If false: continue checking even if
  one parameter gradient has failed. Typically use false for debugging, true for unit tests.
  @param inputs Input arrays to use for forward pass. May be mini-batch data.
  @param labels Labels/targets (output) arrays to use to calculate backprop gradient. May be mini-batch data.
  @return true if gradients are passed, false otherwise.