diff --git a/tensorflow/compiler/mlir/tensorflow/tests/lower_tf.mlir b/tensorflow/compiler/mlir/tensorflow/tests/lower_tf.mlir
index c5f87c602a30403d1b8e82443a8ae5f796b69a91..c2b3d8a353772497e51cc5d39a8165347259d545 100644
--- a/tensorflow/compiler/mlir/tensorflow/tests/lower_tf.mlir
+++ b/tensorflow/compiler/mlir/tensorflow/tests/lower_tf.mlir
@@ -3,8 +3,8 @@
 // CHECK-LABEL: invert_permutation
 func @invert_permutation(%arg0: tensor<5xi32>) -> tensor<5xi32> {
   // CHECK-NEXT: %[[UPDATES:.*]] = "tf.Const"() {value = dense<[0, 1, 2, 3, 4]> : tensor<5xi32>} : () -> tensor<5xi32>
-  // CHECK-NEXT: %[[PERM:.*]] = "tf.Const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
-  // CHECK-NEXT: %[[INDICES:.*]] = "tf.Transpose"(%arg0, %[[PERM]]) : (tensor<5xi32>, tensor<2xi32>) -> tensor<5x1xi32>
+  // CHECK-NEXT: %[[SHAPE:.*]] = "tf.Const"() {value = dense<[5, 1]> : tensor<2xi32>} : () -> tensor<2xi32>
+  // CHECK-NEXT: %[[INDICES:.*]] = "tf.Reshape"(%arg0, %[[SHAPE]]) : (tensor<5xi32>, tensor<2xi32>) -> tensor<5x1xi32>
   // CHECK-NEXT: "tf.TensorScatterUpdate"(%arg0, %[[INDICES]], %[[UPDATES]]) : (tensor<5xi32>, tensor<5x1xi32>, tensor<5xi32>) -> tensor<5xi32>
   %0 = "tf.InvertPermutation"(%arg0) : (tensor<5xi32>) -> tensor<5xi32>
   return %0 : tensor<5xi32>
diff --git a/tensorflow/compiler/mlir/tensorflow/transforms/lower_tf.cc b/tensorflow/compiler/mlir/tensorflow/transforms/lower_tf.cc
index f934e2ac169ccefa5894cfec38d150380bd80828..c0de6f557ab20aae81a5531a390d68a545d62641 100644
--- a/tensorflow/compiler/mlir/tensorflow/transforms/lower_tf.cc
+++ b/tensorflow/compiler/mlir/tensorflow/transforms/lower_tf.cc
@@ -253,8 +253,8 @@ class LowerDynamicStitchOp : public OpRewritePattern<TF::DynamicStitchOp> {
 //   %delta = "tf.Const"() {value = dense<1> : tensor<i32>}
 //   %updates = "tf.Range"(%start, %limit, %delta) :
 //     (tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<5xi32>
-//   %perm = "tf.Const"() {value = dense<[1, 0]> : tensor<2xi32>}
-//   %indices = "tf.Transpose"(%x, %perm) : (tensor<5xi32, tensor<2xi32) ->
+//   %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>
@@ -268,13 +268,12 @@ class LowerInvertPermutationOp
   LogicalResult matchAndRewrite(TF::InvertPermutationOp op,
                                 PatternRewriter &rewriter) const override {
     Location loc = op.getLoc();
-    auto x_type = op.x().getType().cast<TensorType>();
-    Type int_type = x_type.getElementType();  // Could be i32 or i64.
-
+    auto x_type = op.x().getType().dyn_cast<RankedTensorType>();
     // x input must have static shape.
-    if (!x_type.hasStaticShape()) {
+    if (!x_type || !x_type.hasStaticShape()) {
       return failure();
     }
+    Type int_type = x_type.getElementType();  // Could be i32 or i64.
 
     auto result_type = x_type;
     auto start =
@@ -287,13 +286,11 @@ class LowerInvertPermutationOp
     auto updates =
         rewriter.create<TF::RangeOp>(loc, result_type, start, limit, delta);
 
-    auto perm_type = RankedTensorType::get({2}, int_type);
-    auto perm = rewriter.create<TF::ConstOp>(
-        loc, DenseElementsAttr::get(perm_type, {1, 0}));
-    auto transposed_x_type =
-        RankedTensorType::get({x_type.getShape()[0], 1}, int_type);
-    auto indices =
-        rewriter.create<TF::TransposeOp>(loc, transposed_x_type, op.x(), perm);
+    auto shape_type = RankedTensorType::get({2}, rewriter.getIntegerType(32));
+    auto shape = rewriter.create<TF::ConstOp>(
+        loc, DenseElementsAttr::get(
+                 shape_type, {static_cast<int>(x_type.getDimSize(0)), 1}));
+    auto indices = rewriter.create<TF::ReshapeOp>(loc, op.x(), shape);
 
     rewriter.replaceOpWithNewOp<TF::TensorScatterUpdateOp>(
         op, result_type, op.x(), indices, updates);