diff --git a/frontends/pytorch/csrc/builder/ivalue_importer.cpp b/frontends/pytorch/csrc/builder/ivalue_importer.cpp
index f07a4f507..8b87ed873 100644
--- a/frontends/pytorch/csrc/builder/ivalue_importer.cpp
+++ b/frontends/pytorch/csrc/builder/ivalue_importer.cpp
@@ -508,12 +508,18 @@ void IValueImporter::importCompilationUnit(torch::jit::CompilationUnit *cu) {
           MlirType dtype = TypeMapper(context).mapFromTorchScalarType(
               mlirLocationUnknownGet(context), *maybeDtype);
           MlirType typeBound;
+          // `std::vector`'s `.data()` method can return nullptr when the
+          // size is 0. This triggers the "nothing known about sizes" case in
+          // the C API constructor, when we want the "we know we have 0 sizes"
+          // case. So use a dummy data pointer.
+          int64_t dummy;
+          int64_t *shapeData = shape.size() == 0 ? &dummy : shape.data();
           if (hasValueSemantics) {
             typeBound = npcompTorchValueTensorTypeGet(context, shape.size(),
-                                                      shape.data(), dtype);
+                                                      shapeData, dtype);
           } else {
             typeBound = npcompTorchNonValueTensorTypeGet(context, shape.size(),
-                                                         shape.data(), dtype);
+                                                         shapeData, dtype);
           }
 
           MlirNamedAttribute typeBoundAttr = toMlirNamedAttribute(
diff --git a/frontends/pytorch/test/ivalue_import/annotations/arg-tensor-type-bound.py b/frontends/pytorch/test/ivalue_import/annotations/arg-tensor-type-bound.py
index c883fbd86..1b46128ec 100644
--- a/frontends/pytorch/test/ivalue_import/annotations/arg-tensor-type-bound.py
+++ b/frontends/pytorch/test/ivalue_import/annotations/arg-tensor-type-bound.py
@@ -14,7 +14,7 @@ mb = torch_mlir.ModuleBuilder()
 class TestModule(torch.nn.Module):
     def __init__(self):
         super().__init__()
-    def forward(self, tensor):
+    def forward(self, a, b):
         return
 
 test_module = TestModule()
@@ -24,11 +24,13 @@ annotator = torch_mlir.ClassAnnotator()
 class_type = recursivescriptmodule._c._type()
 # CHECK: func private @__torch__.TestModule.forward(
 # CHECK-SAME: %arg0: !torch.nn.Module<"__torch__.TestModule">,
-# CHECK-SAME: %arg1: !torch.tensor {torch.type_bound = !torch.vtensor<[?,1024],si8>}
+# CHECK-SAME: %arg1: !torch.tensor {torch.type_bound = !torch.vtensor<[?,1024],si8>},
+# CHECK-SAME: %arg2: !torch.tensor {torch.type_bound = !torch.vtensor<[],f32>}
 # CHECK-SAME: ) -> !torch.none
 annotator.annotateArgs(class_type, ['forward'], [
     None,
     ((-1, 1024), torch.int8, True),
+    ((), torch.float, True),
 ])
 
 # # TODO: Automatically handle unpacking Python class RecursiveScriptModule into the underlying ScriptModule.