diff --git a/lib/Conversion/TorchToTcp/Misc.cpp b/lib/Conversion/TorchToTcp/Misc.cpp
index a58e3072..d50b9ef7 100644
--- a/lib/Conversion/TorchToTcp/Misc.cpp
+++ b/lib/Conversion/TorchToTcp/Misc.cpp
@@ -82,6 +82,8 @@ class ConvertAtenBroadcastLikeOps : public OpConversionPattern<AtenOpT> {
 
     SmallVector<int64_t> axes;
     SmallVector<Value> resultShape;
+    ArrayRef<int64_t> newInputShape =
+        input.getType().dyn_cast<RankedTensorType>().getShape();
     for (int64_t i = 0; i < static_cast<int64_t>(newDimSizes.size()); ++i) {
       Value newDimSize = newDimSizes[i];
 
@@ -111,9 +113,12 @@ class ConvertAtenBroadcastLikeOps : public OpConversionPattern<AtenOpT> {
               ? true
               : staticDimSize != inputShape[i - newLeadingDims];
 
+      bool isInputDimBroadcastable = newInputShape[i] == 1;
       // Note: The order of checks in this boolean expression matters!
-      if (isNewDim || isDynamicDim ||
-          (!isDimSizePreserved && doesDimSizeChange)) {
+      bool isOutputDimBroadcastable =
+          isNewDim || isDynamicDim ||
+          (!isDimSizePreserved && doesDimSizeChange);
+      if (isInputDimBroadcastable && isOutputDimBroadcastable) {
         axes.push_back(i);
         newDimSize = rewriter.create<torch::TorchConversion::ToI64Op>(
             op->getLoc(), newDimSize);
diff --git a/test/Conversion/TorchToTcp/misc.mlir b/test/Conversion/TorchToTcp/misc.mlir
index d6c19727..b555a4dd 100644
--- a/test/Conversion/TorchToTcp/misc.mlir
+++ b/test/Conversion/TorchToTcp/misc.mlir
@@ -397,6 +397,27 @@ func.func @torch.aten.broadcast_to(%arg0: !torch.vtensor<[1,2,1,2],f32>) -> !tor
 
 // -----
 
+// CHECK-LABEL:  @torch.aten.broadcast_to_with_dynamic_dim_input(
+// CHECK-SAME:   %[[ARG:.*]]: !torch.vtensor<[?,2736,1],f32>) -> !torch.vtensor<[?,2736,16],f32> {
+// CHECK:        %[[TENSOR:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,2736,1],f32> -> tensor<?x2736x1xf32>
+// CHECK:        %[[CONSTANT:.*]] = torch.constant.int 16
+// CHECK:        %[[CAST0:.*]] = torch_c.to_i64 %[[CONSTANT]]
+// CHECK:        %[[BROADCAST_DIM:.*]] = arith.index_cast %[[CAST0]] : i64 to index
+// CHECK:        %[[AFTER_BROADCAST:.*]] = tcp.broadcast %[[TENSOR]], %[[BROADCAST_DIM]] {axes = [2]} : tensor<?x2736x1xf32>, index -> tensor<?x2736x16xf32>
+// CHECK:        %[[OUT:.*]] = torch_c.from_builtin_tensor %[[AFTER_BROADCAST]] : tensor<?x2736x16xf32> -> !torch.vtensor<[?,2736,16],f32>
+// CHECK:        return %[[OUT]] : !torch.vtensor<[?,2736,16],f32>
+func.func @torch.aten.broadcast_to_with_dynamic_dim_input(%arg0: !torch.vtensor<[?,2736,1],f32>) -> !torch.vtensor<[?,2736,16],f32> {
+  %int0 = torch.constant.int 0
+  %int2736 = torch.constant.int 2736
+  %int16 = torch.constant.int 16
+  %0 = torch.aten.size.int %arg0, %int0 : !torch.vtensor<[?,2736,1],f32>, !torch.int -> !torch.int
+  %1 = torch.prim.ListConstruct %0, %int2736, %int16 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
+  %2 = torch.aten.broadcast_to %arg0, %1 : !torch.vtensor<[?,2736,1],f32>, !torch.list<int> -> !torch.vtensor<[?,2736,16],f32>
+  return %2 : !torch.vtensor<[?,2736,16],f32>
+}
+
+// -----
+
 // CHECK-LABEL:  @torch.aten.broadcast_to_dynamic_dim(
 // CHECK-SAME:   %[[ARG0:.*]]: !torch.vtensor<[1,2],f32>, %[[ARG1:.*]]: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?,2],f32> {
 // CHECK-DAG:    %[[ARG1_T:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[?],f32> -> tensor<?xf32>