diff --git a/lib/Dialect/Torch/Transforms/MaximizeValueSemantics.cpp b/lib/Dialect/Torch/Transforms/MaximizeValueSemantics.cpp
index 03eded344..554ef263f 100644
--- a/lib/Dialect/Torch/Transforms/MaximizeValueSemantics.cpp
+++ b/lib/Dialect/Torch/Transforms/MaximizeValueSemantics.cpp
@@ -50,7 +50,25 @@ public:
       if (auto copyToValueTensor = dyn_cast<CopyToValueTensorOp>(user)) {
         rewriter.replaceOp(copyToValueTensor, {currentlyHeldValueTensor});
       } else if (auto overwriteTensor = dyn_cast<OverwriteTensorOp>(user)) {
-        currentlyHeldValueTensor = overwriteTensor.value();
+        Location loc = user->getLoc();
+        Value overwriter = overwriteTensor.value();
+        Value overwritten = overwriteTensor.overwritten();
+        Type overwrittenType = overwritten.getType()
+                                   .dyn_cast<NonValueTensorType>()
+                                   .getWithValueSemantics();
+
+        // Sometimes the overwriter tensor has a different type from the
+        // overwritten tensor. This can happen, for example, if one tensor
+        // has dynamic shape and the other has a static shape. Since the goal
+        // of this pattern is to replace uses of the overwritten tensor with
+        // overwriter tensor, here we cast the overwriter to the type of the
+        // overwritten, to avoid type mismatches later on in the graph.
+        auto savedIP = rewriter.saveInsertionPoint();
+        rewriter.setInsertionPointAfter(overwriteTensor);
+        currentlyHeldValueTensor = rewriter.create<TensorStaticInfoCastOp>(
+            loc, overwrittenType, overwriter);
+        rewriter.restoreInsertionPoint(savedIP);
+
         rewriter.eraseOp(overwriteTensor);
       } else {
         llvm_unreachable("only those ops supported!");
diff --git a/test/Dialect/Torch/maximize-value-semantics.mlir b/test/Dialect/Torch/maximize-value-semantics.mlir
index fb489cdfb..151944b42 100644
--- a/test/Dialect/Torch/maximize-value-semantics.mlir
+++ b/test/Dialect/Torch/maximize-value-semantics.mlir
@@ -55,6 +55,22 @@ func @unmodeled_mutation(%arg0: !torch.vtensor, %arg1: !torch.vtensor) -> !torch
   return %result : !torch.vtensor
 }
 
+// CHECK-LABEL:   func @mutation_with_tensor_of_different_type(
+// CHECK-SAME:                                                 %[[T_0:.*]]: !torch.vtensor<[2],f32>,
+// CHECK-SAME:                                                 %[[T_1:.*]]: !torch.vtensor<[?],f32>) -> !torch.vtensor<[2],f32> {
+// CHECK:           %[[ONE:.*]] = torch.constant.int 1
+// CHECK:           %[[ADD:.*]] = torch.aten.add.Tensor %[[T_0]], %[[T_1]], %[[ONE]] : !torch.vtensor<[2],f32>, !torch.vtensor<[?],f32>, !torch.int -> !torch.vtensor<[?],f32>
+// CHECK:           %[[STATIC_CAST:.*]] = torch.tensor_static_info_cast %[[ADD]] : !torch.vtensor<[?],f32> to !torch.vtensor<[2],f32>
+// CHECK:           return %[[STATIC_CAST]] : !torch.vtensor<[2],f32>
+func @mutation_with_tensor_of_different_type(%t0: !torch.vtensor<[2],f32>, %t1: !torch.vtensor<[?],f32>) -> !torch.vtensor<[2],f32> {
+  %one = torch.constant.int 1
+  %t0_copy = torch.copy.to_tensor %t0 : !torch.tensor<[2],f32>
+  %add = torch.aten.add.Tensor %t0, %t1, %one : !torch.vtensor<[2],f32>, !torch.vtensor<[?],f32>, !torch.int -> !torch.vtensor<[?],f32>
+  torch.overwrite.tensor %add overwrites %t0_copy : !torch.vtensor<[?],f32>, !torch.tensor<[2],f32>
+  %t0_value_copy = torch.copy.to_vtensor %t0_copy : !torch.vtensor<[2],f32>
+  return %t0_value_copy : !torch.vtensor<[2],f32>
+}
+
 // We don't yet handle nontrivial cases involving control flow.
 // CHECK-LABEL:   func @unimplemented_control_flow(
 // CHECK:           torch.copy.to_vtensor