// CHECK: %[[CONV:.+]] = stablehlo.convolution(%[[ARG0]], %[[DQ]])
// CHECK{LITERAL}: dim_numbers = [b, 0, 1, f]x[i, 0, 1, o]->[b, 0, 1, f], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 4 : i64}
// CHECK-SAME: (tensor<1x3x3x4xf32>, tensor<1x3x3x4xf32>) -> tensor<1x3x3x4xf32>

// CHECK:  return
}

func.func @reverse_v2(%arg0: tensor<1x2x3x4xf32>, %arg1: tensor<1xi32>) -> tensor<1x2x3x4xf32> {
  %0 = "tf.ReverseV2"(%arg0, %arg1) : (tensor<1x2x3x4xf32>, tensor<1xi32>) -> tensor<1x2x3x4xf32>
  func.return %0 : tensor<1x2x3x4xf32>

// CHECK-LABEL:reverse_v2
// CHECK:  "tfl.reverse_v2"(%arg0, %arg1) : (tensor<1x2x3x4xf32>, tensor<1xi32>) -> tensor<1x2x3x4xf32>
// CHECK:  return
}

// -----

func.func @testPReluWrongOutputShape(%arg0: tensor<1x2x3x4xf32>, %arg1: tensor<2x3x4xf32>) -> tensor<1x2x3x5xf32> {
  // expected-error @+1 {{'tfl.prelu' op result type '1x2x3x5' not broadcast compatible with broadcasted operands's shapes '1x2x3x4'}}
  %0 = "tfl.prelu"(%arg0, %arg1) : (tensor<1x2x3x4xf32>, tensor<2x3x4xf32>) -> tensor<1x2x3x5xf32>
  func.return %0 : tensor<1x2x3x5xf32>
}

// -----

  //
  // For example,
  //
  //   %shape  = tf.Shape(%arg)                   // %arg: tensor<?x2x3x1xf32>
  //   %height = tf.StridedSlice(%shape, 1, 2, 1)
  //
  // In this case %height can be replaced with a constant 2.
  //
  // Or,
  //
  //   %shape  = tf.Shape(%arg)                   // %arg: tensor<?x2x3x1xf32>
  //   %spatial_shape = tf.StridedSlice(%shape, 1, 3, 1)
  //