def inspect_optimizer(self):
        """ Prints the summary for the optimizer.
        """
        optimizer = self.nn_spec.updateParams.optimizer
        optimizer_type = optimizer.WhichOneof("OptimizerType")
        print("Optimizer Type: {}".format(optimizer_type))
        if optimizer_type == "sgdOptimizer":
            lr = optimizer.sgdOptimizer.learningRate
            batch = optimizer.sgdOptimizer.miniBatchSize
            momentum = optimizer.sgdOptimizer.momentum
            print(
                "lr: {}, min: {}, max: {}".format(
                    lr.defaultValue, lr.range.minValue, lr.range.maxValue
                )
            )
            print(
                "batch: {}, allowed_set: {}".format(
                    batch.defaultValue, batch.set.values
                )
            )
            print(
                "momentum: {}, min: {}, max: {}".format(
                    momentum.defaultValue,
                    momentum.range.minValue,
                    momentum.range.maxValue,
                )
            )
        elif optimizer_type == "adamOptimizer":
            lr = optimizer.adamOptimizer.learningRate
            batch = optimizer.adamOptimizer.miniBatchSize
            beta1 = optimizer.adamOptimizer.beta1
            beta2 = optimizer.adamOptimizer.beta2
            eps = optimizer.adamOptimizer.eps
            print(
                "lr: {}, min: {}, max: {}".format(
                    lr.defaultValue, lr.range.minValue, lr.range.maxValue
                )
            )
            print(
                "batch: {}, allowed_set: {}".format(
                    batch.defaultValue, batch.set.values
                )
            )
            print(
                "beta1: {}, min: {}, max: {}".format(
                    beta1.defaultValue, beta1.range.minValue, beta1.range.maxValue
                )
            )
            print(
                "beta2: {}, min: {}, max: {}".format(
                    beta2.defaultValue, beta2.range.minValue, beta2.range.maxValue
                )
            )
            print(
                "epsilon: {}, min: {}, max: {}".format(
                    eps.defaultValue, eps.range.minValue, eps.range.maxValue
                )
            )
 Prints the summary for the optimizer.
        
coremltools/models/neural_network/builder.py