%5 = "mhlo.broadcast_in_dim"(%4) {broadcast_dimensions = dense<[2, 3]> : tensor<2xi64>} : (tensor<6x6xf32>) -> tensor<1x3x6x6xf32>
  %6 = mhlo.divide %3, %5 : tensor<1x3x6x6xf32>
  return %6 : tensor<1x3x6x6xf32>
}

// CHECK-LABEL:   func.func @avg_pool2d_2(
// CHECK-SAME:                               %[[VAL_0:.*]]: tensor<1x3x6x6xf32>) -> tensor<*xf32> {
// CHECK:           %[[VAL_1:.*]] = arith.constant dense<[0, 2, 3, 1]> : tensor<4xi32>

  return %2 : tensor<1x8x4x4xf32>
}
// CHECK-DAG: %[[CONST:.+]] = stablehlo.constant {{.*}} : tensor<3x3x8x8xf32>
// CHECK-DAG: %[[TRANSPOSE_0:.+]] = stablehlo.transpose %[[ARG]], dims = [0, 2, 3, 1] : (tensor<1x8x4x4xf32>) -> tensor<1x4x4x8xf32>

// permutated to match the new input shape.
// CHECK: (tensor<1x16x16x4xf32>, tensor<f32>) -> tensor<1x8x8x4xf32>

// Check that a `stablehlo.transpose` is added to the result to match the shape
// of the users.
// CHECK: %[[TRANSPOSE:.+]] = stablehlo.transpose %[[REDUCE_WINDOW]], dims = [0, 3, 1, 2] : (tensor<1x8x8x4xf32>) -> tensor<1x4x8x8xf32>
// CHECK: return %[[TRANSPOSE]]

// -----