%6 = "tfl.quantize"(%conv2) {qtype = tensor<1x56x56x32x!quant.uniform<u8:f32, 1.0>>} : (tensor<1x56x56x32xf32>) -> tensor<1x56x56x32x!quant.uniform<u8:f32, 1.0>>

  func.return %6 : tensor<1x56x56x32x!quant.uniform<u8:f32, 1.0>>

// CHECK:             stablehlo.return %[[MAX]] : tensor<f32>
// CHECK:           }) : (tensor<1x114x114x64xf32>, tensor<f32>) -> tensor<1x56x56x64xf32>
// CHECK:           %[[TPOS:.*]] = stablehlo.transpose %[[REDUCE]], dims = [0, 3, 1, 2]
// CHECK:              : (tensor<1x56x56x64xf32>) -> tensor<1x64x56x56xf32>
// CHECK:           return %[[TPOS]] : tensor<1x64x56x56xf32>

func.func @commute_transpose_reduce_window(

  %2 = "tf.MaxPool"(%1)
       {
         data_format = "NHWC", ksize = [1, 3, 3, 1],
         padding = "SAME", strides = [1, 2, 2, 1]
       } : (tensor<1x112x112x64xf32>) -> tensor<1x56x56x64xf32>

  func.return %2 : tensor<1x56x56x64xf32>
}

// CHECK-LABEL: func @fold_into_mean
func.func @fold_into_mean(%arg0: tensor<1x64x112x112xf32>) -> tensor<1x64xf32> {

  %1 = "tfl.dequantize"(%arg1) : (tensor<1x56x56x24x!quant.uniform<u8:f32, 0.40149296779258581:136>>) -> tensor<1x56x56x24xf32>
  %2 = tfl.add %0, %1 {fused_activation_function = "NONE"} : tensor<1x56x56x24xf32> loc("Block")

  func.return %5 : tensor<1x56x56x16xf32>

// CHECK: tfl.hard_swish
// CHECK: tfl.depthwise_conv_2d
}

// CHECK-LABEL: squeezeToReshape
func.func @squeezeToReshape(%arg0: tensor<1x1x2xf32>) -> tensor<2xf32> {