func.func @testSpaceToDepthF32(%arg0: tensor<1x2x2x1xf32>) -> tensor<1x1x1x4xf32> {
  // CHECK: %[[ARG:.*]]: tensor<1x2x2x1xf32>
  // CHECK: "tfl.space_to_depth"(%[[ARG]]) <{block_size = 2 : i32}> : (tensor<1x2x2x1xf32>) -> tensor<1x1x1x4xf32>
  %0 = "tfl.space_to_depth"(%arg0) {block_size = 2: i32} : (tensor<1x2x2x1xf32>) -> tensor<1x1x1x4xf32>
  func.return %0 : tensor<1x1x1x4xf32>
}

// -----

^bb0(%arg0: tensor<1x7x7x16xf32>):
  // expected-error @+1 {{requires attribute 'padding'}}
  %0 = "tf.AvgPool"(%arg0) {T = "tfdtype$DT_FLOAT", ksize = [1, 7, 7, 1], strides = [1, 1, 1, 1]} : (tensor<1x7x7x16xf32>) -> tensor<1x1x1x16xf32>
  func.return %0 : tensor<1x1x1x16xf32>
}

// -----

func.func @testAvgPoolWrongPadding(tensor<1x7x7x16xf32>) -> tensor<1x1x1x16xf32> {

  func.func @simple_folding(%arg0: tensor<1x1x1x1xi32>, %arg1: tensor<1x1x1x1xf32>) -> tensor<?x?x?x?xf32> {
    // CHECK: %[[SHAPE:.*]] = "tf.Shape"
    // CHECK: %[[CONV:.*]] = "tf.Conv2DBackpropInput"(%[[SHAPE]]
    // CHECK-SAME: (tensor<4xi32>, tensor<1x1x1x1xf32>, tensor<1x1x1x1xf32>) -> tensor<1x1x1x1xf32>
    // CHECK: return %[[CONV]] : tensor<1x1x1x1xf32>
    %0 = "tf.Shape"(%arg0) : (tensor<1x1x1x1xi32>) -> tensor<4xi32>

  %0:2 = "tf.SplitV"(%arg0, %arg1, %arg2) : (tensor<1x4x3x3xf32>, tensor<2xi32>, tensor<i32>) -> (tensor<1x4x2x3xf32>, tensor<1x4x1x3xf32>)
  func.return %0#0 : tensor<1x4x2x3xf32>

  // CHECK-LABEL: splitv
  // CHECK: "tfl.split_v"(%arg0, %arg1, %arg2) <{num_splits = 2 : i32}> : (tensor<1x4x3x3xf32>, tensor<2xi32>, tensor<i32>) -> (tensor<1x4x2x3xf32>, tensor<1x4x1x3xf32>)
}

  %0 = stablehlo.constant() {value = dense<3> : tensor<2x2x1x3xi8>} : () -> tensor<2x2x1x3x!quant.uniform<i8:f32:3, {3.000000e+00, 3.000000e+00, 3.000000e+00}>>
  %1 = stablehlo.constant() {value = dense<5> : tensor<1x1x1x3xi32>} : () -> tensor<1x1x1x3x!quant.uniform<i32:f32:3, {2.000000e+00, 2.000000e+00, 2.000000e+00}>>