// deferred to the result. The attributes are permutated according to the new
// input shape. This test is similar to the test above with the difference that
// the `stablehlo.reduce_window` has explicit optional attributes:
// `base_dilations` and `window_dilations`.

// CHECK-LABEL: reduce_window_max_activation_transpose_explicit_optional_attrs
func.func @reduce_window_max_activation_transpose_explicit_optional_attrs(

                              .HostMemory("window_dimensions")                 \
                              .HostMemory("window_strides")                    \
                              .HostMemory("base_dilations")                    \
                              .HostMemory("window_dilations")                  \
                              .HostMemory("padding")                           \

              op->window_strides, builder)));
    }
    if (!op->base_dilations.empty()) {
      attributes.emplace_back(builder.getNamedAttr(
          "base_dilations",
          BuildVhloTensorV1Attr(
              {static_cast<int64_t>(op->base_dilations.size())},
              op->base_dilations, builder)));
    }
    if (!op->window_dilations.empty()) {

       field_name == "interior_padding"))
    return true;
  if (op_name == "stablehlo.reduce_window" &&
      (field_name == "window_dimensions" || field_name == "window_strides" ||
       field_name == "base_dilations" || field_name == "window_dilations"))
    return true;
  if (op_name == "stablehlo.select_and_scatter" &&
      (field_name == "window_dimensions" || field_name == "window_strides"))
    return true;

            /*window_strides=*/
            PermuteI64ArrayAttr(rewriter, op.getWindowStrides(),
                                kNchwToNhwcPermutation),
            /*base_dilations=*/
            PermuteI64ArrayAttr(rewriter, op.getBaseDilations(),
                                kNchwToNhwcPermutation),
            /*window_dilations=*/

  %2 = "mhlo.reduce_window"(%arg0, %0) ({
    ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):
      %5 = mhlo.add %arg1, %arg2 : tensor<f32>
      "mhlo.return"(%5) : (tensor<f32>) -> ()
    }) {
    base_dilations = dense<1> : tensor<4xi64>,
    padding = dense<0> : tensor<4x2xi64>,
    window_dilations = dense<1> : tensor<4xi64>,
    window_dimensions = dense<[1, 3, 3, 1]> : tensor<4xi64>,

      // $padding_low, $padding_high, $padding_interior
      CompileTimeConstantOperand<TF::XlaPadOp, 2, 3, 4>,
      // $window_dimensions, $window_strides, $base_dilations,
      // $window_dilations, $padding
      CompileTimeConstantOperand<TF::XlaReduceWindowOp, 2, 3, 4, 5, 6>,
      // $dim_index
      CompileTimeConstantOperand<TF::XlaRemoveDynamicDimensionSizeOp, 1>,

//CHECK-NEXT: return %0 : tensor<2xf32>
//CHECK-NEXT:}

func.func @reduce_window(%arg0: tensor<1x160x1xf32>, %arg1: tensor<f32>) -> tensor<1x160x1xf32> {
  %0 = "vhlo.reduce_window_v1"(%arg0, %arg1) <{base_dilations = #vhlo.tensor_v1<dense<1> : tensor<3xi64>>,
        padding = #vhlo.tensor_v1<dense<[[0, 0], [159, 0], [0, 0]]> : tensor<3x2xi64>>,
        window_dilations = #vhlo.tensor_v1<dense<1> : tensor<3xi64>>,

  DenseElementsAttr window_dimensions, window_strides, base_dilations,
      window_dilations, padding;
  if (input_ty.hasStaticShape() &&
      matchPattern(op.getWindowDimensions(), m_Constant(&window_dimensions)) &&
      matchPattern(op.getWindowStrides(), m_Constant(&window_strides)) &&
      matchPattern(op.getBaseDilations(), m_Constant(&base_dilations)) &&

    DenseElementsAttr window_dimensions, window_strides, base_dilations,
        window_dilations, padding;
    if (!(matchPattern(op.getWindowDimensions(),
                       m_Constant(&window_dimensions)) &&
          matchPattern(op.getWindowStrides(), m_Constant(&window_strides)) &&
          matchPattern(op.getBaseDilations(), m_Constant(&base_dilations)) &&
          matchPattern(op.getWindowDilations(),