using ops::FractionalAvgPool;
using ops::FractionalMaxPool;
using ops::FusedBatchNormV3;
using ops::L2Loss;
using ops::LogSoftmax;
using ops::LRN;
using ops::MaxPool;
using ops::MaxPool3D;
using ops::MaxPoolV2;
using ops::Placeholder;
using ops::Relu;
using ops::Relu6;
using ops::Selu;
using ops::Softmax;
using ops::Softplus;
using ops::Softsign;

class NNGradTest : public ::testing::Test {
 protected:

  grad_outputs->push_back(dx);
  grad_outputs->push_back(NoGradient());
  grad_outputs->push_back(NoGradient());
  return scope.status();
}
REGISTER_GRADIENT_OP("MaxPoolV2", MaxPoolGradV2Helper);

Status MaxPool3DGradHelper(const Scope& scope, const Operation& op,
                           const std::vector<Output>& grad_inputs,
                           std::vector<Output>* grad_outputs) {

      "MaxPool",
      "MaxPool3D",
      "MaxPool3DGrad",
      "MaxPool3DGradGrad",
      "MaxPoolGrad",
      "MaxPoolGradGrad",
      "MaxPoolGradGradV2",
      "MaxPoolGradV2",
      "MaxPoolV2",
      "Multinomial",
      "NextAfter",
      "NonMaxSuppressionV3",
      "NonMaxSuppressionV4",
      "ParallelDynamicStitch",
      "ParameterizedTruncatedNormal",
      "PartitionedCall",

  let results = (outs
    Res<TF_IntOrFpTensor, [{Gradients w.r.t. the input to `max_pool`.}]>:$output
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_MaxPoolV2Op : TF_Op<"MaxPoolV2", [Pure]> {
  let summary = "Performs max pooling on the input.";

  let arguments = (ins