}

func.func @reduce_min(%arg0: tensor<8x16x16xf32>, %arg1: tensor<2xi32>) -> tensor<?xf32> {
  %0 = "tf.Min"(%arg0, %arg1) {keep_dims = false} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32>
  func.return %0 : tensor<?xf32>

  // CHECK-LABEL: reduce_min
  // CHECK: "tfl.reduce_min"(%arg0, %arg1) <{keep_dims = false}> : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32>

  let results = (outs
    TFL_TensorOf<[F32, I32, I64, QI8, QUI8, TFL_Quint8, QI16]>:$output);

  let hasOptions = 1;
  let customOption = "ReducerOptions";
}

def TFL_ReduceMaxOp: TFL_Op<"reduce_max", [
    PredOpTrait<"input and output must have same element type",
      TFL_TCresVTEtIsSameAsOp<0, 0>>,
    Pure,
    QuantizableResult,
    SameOperandsAndResultsScale]> {

    average_min_max_output = converted_model_average_min_max.signatures[
        'serving_default'
    ](input_tensor=sample_input)['output']

    def get_mean_square_error(x, y):
      ret = tensorflow.reduce_mean(tensorflow.square(tensorflow.subtract(x, y)))
      try:
        ret = ret.numpy()
      except AttributeError:
        ret = ret.eval()
      return ret