%10 = "tf.Cast"(%9) {Truncate = false} : (tensor<2x2xi8>) -> tensor<2x2xi32>
    %11 = "tf.Sub"(%10, %cst_4) : (tensor<2x2xi32>, tensor<i32>) -> tensor<2x2xi32>
    %12 = "tf.Identity"(%4) : (tensor<2x2xi8>) -> tensor<2x2xi8>
    %13 = "tf.Cast"(%12) {Truncate = false} : (tensor<2x2xi8>) -> tensor<2x2xi32>
    %14 = "tf.Sub"(%13, %cst_2) : (tensor<2x2xi32>, tensor<i32>) -> tensor<2x2xi32>

// -----

func.func @transpose(%arg0 : tensor<2x2xi32>, %arg1 : tensor<2xi32>) -> tensor<2x2xi32> {
  %0 = "tfl.transpose"(%arg0, %arg1) : (tensor<2x2xi32>, tensor<2xi32>) -> tensor<2x2xi32>
  func.return %0 : tensor<2x2xi32>
}

// -----

// CHECK-LABEL: transpose_with_output_that_has_dynamic_sizes
func.func @transpose_with_output_that_has_dynamic_sizes(%arg0 : tensor<2x2xi32>, %arg1 : tensor<2xi32>) -> tensor<?x?xi32> {

    %0 = stablehlo.constant dense<3.000000e+00> : tensor<1x1xf32>  // Input inverse scale.
    %1 = stablehlo.constant dense<1> : tensor<1x1xi8>  // Input zero point.
    %2 = stablehlo.constant dense<5> : tensor<2x3xi32>  // Quantized filter - the pattern expects i8 but i32 is given.
    %3 = stablehlo.constant dense<4> : tensor<1x3xi32>  // Precalculated z1 * q2.

  // CHECK: %[[IND_RESHPE:.*]] = "tf.Reshape"(%[[ARG1]], %[[IND_SHAPE]]) : (tensor<5xi32>, tensor<2xi32>) -> tensor<5x1xi32>
  // CHECK: %[[SC:.*]] = "tf.TensorScatterUpdate"(%[[BUFFER]], %[[IND_RESHPE]], %[[ARG2]]) : (tensor<10x8x9xf32>, tensor<5x1xi32>, tensor<5x8x9xf32>) -> tensor<10x8x9xf32>