// CHECK-NEXT: %[[UPDATE_SHAPE:.*]] = "tf.Const"() <{value = dense<[1, 2]> : tensor<2xi32>}> : () -> tensor<2xi32>
  // CHECK-NEXT: %[[UPDATE_SLICE:.*]] = "tf.Reshape"(%[[PUSH_VAL]], %[[UPDATE_SHAPE]]) : (tensor<2xi32>, tensor<2xi32>) -> tensor<1x2xi32>
  // CHECK-NEXT: %[[ZERO_INDS:.*]] = "tf.Const"() <{value = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>

  %5 = "tf.Identity"(%4) {device = ""} : (tensor<2x4xf32>) -> tensor<2x4xf32>
  %6 = "tf.Identity"(%5) {device = ""} : (tensor<2x4xf32>) -> tensor<2x4xf32>
  func.return %6 : tensor<2x4xf32>

  // CHECK-LABEL: QuantDequantTranspose
  // CHECK-DAG: %[[CST:.*]] = "tf.Const"() <{value = dense<[1, 0]> : tensor<2xi32>}> : () -> tensor<?xi32>

  %0 = stablehlo.uniform_dequantize %arg : (tensor<2x2x!quant.uniform<i8:f32, 1.000000e+0:8>>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}
// CHECK-LABEL: uniform_dequantize_op
// CHECK: %[[DEQUANT:.+]] = "tfl.dequantize"({{.*}}) : (tensor<2x2x!quant.uniform<i8:f32, 1.000000e+00:8>>) -> tensor<2x2xf32>
// CHECK: return %[[DEQUANT]]

// -----

//     (tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<5xi32>
//   %shape = "tf.Const"() {value = dense<[5, 1]> : tensor<2xi32>}
//   %indices = "tf.Reshape"(%x, %shape) : (tensor<5xi32, tensor<2xi32) ->
//     tensor<5x1xi32>
//   "tf.TensorScatterUpdate"(%x, %indices, %updates) :
//     (tensor<5xi32>, tensor<5x1xi32>, tensor<5xi32>) -> tensor<5xi32>
//
class LowerInvertPermutationOp : public RewritePattern {
 public:

  %254 = "tf.Tile"(%218, %253) {device = ""} : (tensor<?x1xi64>, tensor<2xi32>) -> tensor<?x?xi64>
  %255 = "tf.Shape"(%254) {device = ""} : (tensor<?x?xi64>) -> tensor<2xi32>

  return {};
}

// Convert Pack to Reshape when there is only one operand to be packed.
// For example,
//
//   %0 = tf.Pack(%input) {axis = 0} // %input : tensor<2x3xf32>
//
// can be canonicalized to
//
//   %shape = "tf.Const"() {value = dense<[1, 2, 3]> : tensor<3xi64>}
//   %0 = tf.Reshape(%input, %shape)
struct ConvertPackToReshape : public OpRewritePattern<PackOp> {