// CHECK: <{window_dimensions = array<i64: 1, 2, 2, 1>, window_strides = array<i64: 1, 2, 2, 1>}>
// CHECK: ^bb0(%[[REDUCE_ARG_0:.+]]: tensor<f32>, %[[REDUCE_ARG_1:.+]]: tensor<f32>):
// CHECK: %[[MAX:.+]] = stablehlo.maximum %[[REDUCE_ARG_0]], %[[REDUCE_ARG_1]]
// CHECK: stablehlo.return %[[MAX]]

// Check that the attributes window_dimensions & window_strides are also

    // CHECK: %[[REDUCE:.*]] = "stablehlo.reduce_window"(%[[CALL]], %[[Q0]])
    // CHECK{LITERAL}: padding = dense<[[0, 0], [1, 1], [1, 1], [0, 0]]> : tensor<4x2xi64>
    // CHECK-SAME: window_dimensions = array<i64: 1, 3, 3, 1>
    // CHECK: %[[ARG1:.*]]: tensor<!quant.uniform<i8:f32, 3.000000e-01:1>>, %[[ARG2:.*]]: tensor<!quant.uniform<i8:f32, 3.000000e-01:1>>

    return;
  }
  if (const auto* op = op_union.AsStablehloReduceWindowOptions()) {
    if (!op->window_dimensions.empty()) {
      attributes.emplace_back(builder.getNamedAttr(
          "window_dimensions",
          BuildVhloTensorV1Attr(
              {static_cast<int64_t>(op->window_dimensions.size())},
              op->window_dimensions, builder)));
    }
    if (!op->window_strides.empty()) {

  %4 = "stablehlo.reduce_window"(%3, %5) ({  // max pool
  ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):
      %6 = stablehlo.maximum %arg1, %arg2 : tensor<f32>
      stablehlo.return %6 : tensor<f32>
  }) {
    window_dimensions = array<i64: 1, 1, 2, 2>,
    window_strides = array<i64: 1, 1, 2, 2>
  } : (tensor<1x4x5x5xf32>, tensor<f32>) -> tensor<1x4x2x2xf32>
  return %4 : tensor<1x4x2x2xf32>
}

    mhlo.return %7 : tensor<f32>
  }) {window_dimensions = dense<[1, 1, 3, 3]> : tensor<4xi64>} : (tensor<1x3x6x6xf32>, tensor<f32>) -> tensor<1x3x4x4xf32>
  %4 = "mhlo.reduce_window"(%1, %2) ({
  ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):
    %7 = mhlo.add %arg1, %arg2 : tensor<f32>
    mhlo.return %7 : tensor<f32>
  }) {window_dimensions = dense<3> : tensor<2xi64>} : (tensor<6x6xf32>, tensor<f32>) -> tensor<4x4xf32>

    auto new_reduce_window_op =
        rewriter.create<mlir::stablehlo::ReduceWindowOp>(
            op.getLoc(), new_result_type, transpose_op.getOperand(),
            /*init_value=*/op.getOperand(1),
            /*window_dimensions=*/
            PermuteI64ArrayAttr(rewriter, op.getWindowDimensions(),
                                kNchwToNhwcPermutation),
            /*window_strides=*/

  rhs_batching_dimensions : [long];
  lhs_contracting_dimensions : [long];
  rhs_contracting_dimensions : [long];
  precision_config : [StablehloPrecisionConfig];
}

table StablehloReduceWindowOptions{
  window_dimensions : [long];
  window_strides : [long];
  base_dilations : [long];
  window_dilations : [long];
  padding : [long];
  body_subgraph_index : int;
}

table StablehloWhileOptions{

    // CHECK: %[[ZERO:.*]] = mhlo.constant dense<0.000000e+00> : tensor<f32>
    // CHECK: %[[RESULT:.*]] = "mhlo.select_and_scatter"(%[[INPUT]], %[[GRAD]], %[[ZERO]]) <{
    // CHECK-SAME:  padding = dense<0> : tensor<4x2xi64>, window_dimensions = dense<[1, 2, 2, 1]> : tensor<4xi64>, window_strides = dense<[1, 2, 2, 1]> : tensor<4xi64>
    // CHECK-SAME }> ({
    // CHECK: ^bb0(%[[VALUE_A:.*]]: tensor<f32>, %[[VALUE_B:.*]]: tensor<f32>):