// CHECK:         }

// -----

module {
func.func @constWeight(%arg0: tensor<256x32x32x3xf32>) -> tensor<256x30x30x16xf32> {
  %0 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<16x3x3x3xf32>} : () -> tensor<16x3x3x3xf32>
  %1 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<16xf32>} : () -> tensor<16xf32>

         qtype = tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
    %w = "tfl.dequantize"(%q_w) :
         (tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) ->
         tensor<64x3x3x3xf32>
    %conv = "tfl.conv_2d"(%input_act, %w, %bias)

    but if it is supported, it will be rewritten as:

    %q_w = "tfl.pseudo_qconst"() {

  func.return %rst : tensor<256x8x7x16xf32>

// CHECK-DAG: %[[CONSTANT:.*]] = arith.constant dense<0.000000e+00> : tensor<16xf32>
// CHECK-DAG: %[[CONSTANT0:.*]] = arith.constant dense<0.000000e+00> : tensor<16x3x3x3xf32>
// CHECK: %[[QUANTIZE:.*]] = "tfl.quantize"(%[[CONSTANT0]]) <{qtype = tensor<16x3x3x3x!quant.uniform<u8:f32, 1.000000e+00>>}>
// CHECK: %[[DEQUANTIZE:.*]] = "tfl.dequantize"(%[[QUANTIZE]])

  %arg1: tensor<f32>
) -> tensor<2x4x3x3xf32> {
  %0 = "stablehlo.reduce_window"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<f32>, %arg3: tensor<f32>):
    %1 = stablehlo.maximum %arg2, %arg3 : tensor<f32>
    stablehlo.return %1 : tensor<f32>
  }) {window_dimensions = array<i64: 1, 3, 4, 1>, window_strides = array<i64: 1, 2, 3, 1>} : (tensor<2x9x10x3xf32>, tensor<f32>) -> tensor<2x4x3x3xf32>
  return %0 : tensor<2x4x3x3xf32>
}

//
// ```
// %0 = stablehlo.constant dense<3>
// %1 = stablehlo.constant dense<4>
// %2 = stablehlo.constant dense<2>
// %3 = stablehlo.convolution(%%arg0, %%arg1) :
//          (tensor<?x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<?x3x4x2xf32>
// %4 = stablehlo.get_dimension_size %3, dim = 0 :
//          (tensor<?x3x4x2xf32>) -> tensor<i32>
// %5 = stablehlo.reshape %4 :
//          (tensor<i32>) -> tensor<1xi32>

    %6 = "tf.Cast"(%3) {Truncate = false} : (tensor<2x3x3x1xi32>) -> tensor<2x3x3x1xf32>
    %7 = "tf.DepthwiseConv2dNative"(%5, %6) {dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 2, 2, 1]} : (tensor<1x3x4x3xf32>, tensor<2x3x3x1xf32>) -> tensor<1x2x2x3xf32>
    %8 = "tf.Cast"(%7) : (tensor<1x2x2x3xf32>) -> tensor<1x2x2x3xi32>

^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x3x4xf32>) :
  %0 = "tf.DepthwiseConv2dNative"(%arg0, %arg1) {device = "", name = "MobilenetV2/expanded_conv/depthwise/depthwise", T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 1, 1, 1], padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x4xf32>) -> tensor<256x30x30x12xf32>

}

// -----

// CHECK-LABEL: depthwiseconv_simple
func.func @depthwiseconv_simple(%arg0: tensor<?x4x5x3xf32>, %arg1: tensor<2x2x3x3xf32>) -> tensor<?x3x4x9xf32> {
  // CHECK: %[[RESHAPED_FILTER:.*]] = mhlo.reshape %arg1 : (tensor<2x2x3x3xf32>) -> tensor<2x2x1x9xf32>
  // CHECK: mhlo.convolution(%arg0, %[[RESHAPED_FILTER]])
  // CHECK-SAME: feature_group_count = 3