diff --git a/python/tvm/relay/frontend/tensorflow.py b/python/tvm/relay/frontend/tensorflow.py
index 5ab1f5e91764..ae00a9a77139 100644
--- a/python/tvm/relay/frontend/tensorflow.py
+++ b/python/tvm/relay/frontend/tensorflow.py
@@ -832,24 +832,30 @@ def all_class_impl(
             _op.less(box_range_2d, _op.expand_dims(num_detections, axis=1)), "float32"
         )
 
-        # TODO: support dynamic num_boxes
-        if max_output_boxes_per_batch < max_total_size:
-            arange = _op.arange(
-                _op.const(0, dtype="int64"),
-                _op.const(max_output_boxes_per_batch, dtype="int64"),
-                dtype="int64",
-            )
-            pad = _op.full(
-                _op.const(0, dtype="int64"), (max_total_size - max_output_boxes_per_batch,)
-            )
-            topk_indices = _op.tile(_op.concatenate([arange, pad], 0), tile_batch_reps)
-            nmsed_scores = _op.gather(selected_scores, 1, topk_indices)
-            nmsed_scores = nmsed_scores * valid_mask
-        else:
-            nmsed_scores, topk_indices = _op.topk(
-                selected_scores, k=max_total_size, axis=1, ret_type="both"
-            )
+        def select_topk(do_zero_pad):
+            def true_branch():
+                arange = _op.arange(
+                    _op.const(0, dtype="int64"),
+                    _op.const(max_output_boxes_per_batch, dtype="int64"),
+                    dtype="int64",
+                )
+                pad = _op.full(
+                    _op.const(0, dtype="int64"), (max_total_size - max_output_boxes_per_batch,)
+                )
+                topk_indices = _op.tile(_op.concatenate([arange, pad], 0), tile_batch_reps)
+                nmsed_scores = _op.gather(selected_scores, 1, topk_indices)
+                nmsed_scores = nmsed_scores * valid_mask
+                return nmsed_scores, topk_indices
+
+            def false_branch():
+                return _op.topk(selected_scores, k=max_total_size, axis=1, ret_type="both")
+
+            # TODO(masahi): support dynamic num_boxes
+            # return _expr.If(do_zero_pad, true_branch(), false_branch())
+            return true_branch() if do_zero_pad else false_branch()
 
+        assert isinstance(max_output_boxes_per_batch, int),"dynamic number of boxes not supported yet."
+        nmsed_scores, topk_indices = select_topk(max_output_boxes_per_batch < max_total_size)
         topk_indices = _op.expand_dims(topk_indices, axis=0)
         indices = _op.gather_nd(selected_indices, topk_indices, batch_dims=1)
 
@@ -894,7 +900,7 @@ def _impl(inputs, attr, params, mod):
         q = boxes_shape[2]
         num_classes = scores_shape[2]
 
-        if q == 1:
+        if q == 1 and isinstance(num_anchors, int):
             boxes = _op.squeeze(boxes, axis=[2])
             scores_trans = _op.transpose(scores, [0, 2, 1])
             max_output_boxes_per_batch = num_anchors * num_classes