Do not legalize tf.Conv2DOp when the filter type is not determined.

Currently, the converter generates a wrong constant when the condition is hold.

PiperOrigin-RevId: 340170033
Change-Id: Ia4af732f456254738c76840a32673ecff5f0fb6a

tensorflow/compiler/mlir/lite/tests/prepare-tf.mlir

@@ -639,4 +639,11 @@ func @DontMatchFusedBatchNormV3(%arg0 :tensor<?x576x1x1xf32>, %arg1 : tensor<576
   // CHECK: "tf.FusedBatchNormV3"
 }
 
+// CHECK-LABEL: DoNotConvertConv2DWhenFilterTypeDimIsNotDecided
+func @DoNotConvertConv2DWhenFilterTypeDimIsNotDecided(%arg0 : tensor<?x?x?x96xf32>, %arg1 : tensor<3x3x96x?xf32>) -> tensor<?x?x?x?xf32> {
+  %0 = "tf.Conv2D"(%arg0, %arg1) {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true} : (tensor<?x?x?x96xf32>, tensor<3x3x96x?xf32>) -> tensor<?x?x?x?xf32>
+  return %0 : tensor<?x?x?x?xf32>
+// CHECK: tf.Conv2D
+}
+
 }

tensorflow/compiler/mlir/lite/transforms/prepare_tf.cc

@@ -328,7 +328,9 @@ struct ConvertTFConvOp : public RewritePattern {
     // tensor, for setting depth_multiplier attribute, etc.).
     auto filter = tf_op.filter();
     auto filter_type = filter.getType().template dyn_cast<RankedTensorType>();
-    if (!filter_type || filter_type.getRank() != 4) return failure();
+    if (!filter_type || filter_type.getRank() != 4 ||
+        !filter_type.hasStaticShape())
+      return failure();
 
     // TensorFlow convolution op only has two inputs, while the TFLite one has
     // three, with the bias vector marked as optional. However, TOCO has a