REGISTER_NO_GRADIENT_OP("NonMaxSuppression");
REGISTER_NO_GRADIENT_OP("NonMaxSuppressionV2");
REGISTER_NO_GRADIENT_OP("NonMaxSuppressionV3");
REGISTER_NO_GRADIENT_OP("NonMaxSuppressionV4");
REGISTER_NO_GRADIENT_OP("NonMaxSuppressionV5");

Status ResizeNearestNeighborGradHelper(const Scope& scope, const Operation& op,
                                       const std::vector<Output>& grad_inputs,

}

func.func @non_max_suppression_v5(%arg0: tensor<3x4xf32>, %arg1: tensor<3xf32>, %arg2: tensor<i32>, %arg3: tensor<f32>, %arg4: tensor<f32>, %arg5: tensor<f32>) -> tensor<2xi32> {
  %0:3 = "tf.NonMaxSuppressionV5"(%arg0, %arg1, %arg2, %arg3, %arg4, %arg5) {pad_to_max_output_size = true}: (tensor<3x4xf32>, tensor<3xf32>, tensor<i32>, tensor<f32>, tensor<f32>, tensor<f32>) -> (tensor<2xi32>, tensor<2xf32>, tensor<i32>)

  );

  let hasOptions = 0;
}

// These ops are named NonMaxSuppressionV4 & NonMaxSuppressionV5 to be
// consistent with TensorFlow's naming. They are NOT 'versions' of NMS in the
// sense that one is an incremental change over the other.
// In reality NonMaxSuppressionV5 implements Soft Non Max Suppression and
// NonMaxSuppressionV4 performs hard NMS.

  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
  TF_DerivedOperandTypeAttr T_threshold = TF_DerivedOperandTypeAttr<3>;
}

def TF_NonMaxSuppressionV5Op : TF_Op<"NonMaxSuppressionV5", [Pure]> {
  let summary = [{
Greedily selects a subset of bounding boxes in descending order of score,
  }];

  let description = [{