diff --git a/python/TorchMLIRModule.cpp b/python/TorchMLIRModule.cpp
index 73abf5cd5..36e391867 100644
--- a/python/TorchMLIRModule.cpp
+++ b/python/TorchMLIRModule.cpp
@@ -28,4 +28,8 @@ PYBIND11_MODULE(_torchMlir, m) {
         }
       },
       py::arg("context"), py::arg("load") = true);
+
+  m.def("get_int64_max", []() { return INT64_MAX; });
+
+  m.def("get_int64_min", []() { return INT64_MIN; });
 }
diff --git a/python/torch_mlir/extras/fx_importer.py b/python/torch_mlir/extras/fx_importer.py
index 6f936e50e..c498e0437 100644
--- a/python/torch_mlir/extras/fx_importer.py
+++ b/python/torch_mlir/extras/fx_importer.py
@@ -78,6 +78,16 @@ except ModuleNotFoundError:
     # conditional.
     ml_dtypes = None
 
+try:
+    from torch.utils._sympy.numbers import int_oo, IntInfinity, NegativeIntInfinity
+except ModuleNotFoundError:
+    # This commit on PyTorch repo introduced IntInfinity and NegativeIntInfinity:
+    # https://github.com/pytorch/pytorch/commit/2229884102ac95c9dda0aeadbded1b04295d892e
+    # Required module may not be present in the stable version of PyTorch.
+    int_oo = None
+    IntInfinity = None
+    NegativeIntInfinity = None
+
 from torch.fx.node import (
     Argument as NodeArgument,
 )
@@ -125,6 +135,8 @@ from ..dialects import (
     func as func_dialect,
 )
 
+from .._mlir_libs._torchMlir import get_int64_max, get_int64_min
+
 __all__ = [
     "FxImporter",
 ]
@@ -1165,22 +1177,32 @@ class ContextCache:
         self, prog: torch.export.ExportedProgram
     ) -> Dict[str, RangeConstraint]:
 
+        # Recent PyTorch versions use `int_oo` to represent integer infinity.
+        # Older PyTorch versions like PyTorch stable version may not have
+        # `int_oo` defined just yet.
+        infs = (sympy.oo, int_oo) if int_oo is not None else (sympy.oo,)
+
         def _sympy_int_to_int(val: sympy.Expr, adjust_func: Callable):
             # Convert simple sympy Integers into concrete int
-            if val == sympy.oo:
-                return math.inf
-            if val == -sympy.oo:
-                return -math.inf
+            if val in infs:
+                return get_int64_max()
+            if val in tuple(-inf for inf in infs):
+                return get_int64_min()
             if isinstance(val, sympy.Integer):
                 return int(val)
             # TODO: Remove this adjustment when fractional ranges are removed
             return adjust_func(val)
 
         contains_symbolic_ints = False
+        sym_int_types = (
+            (sympy.Integer, IntInfinity, NegativeIntInfinity)
+            if IntInfinity is not None
+            else sympy.Integer
+        )
         for val in prog.range_constraints.values():
             if (
-                isinstance(val.lower, sympy.Integer)
-                and isinstance(val.upper, sympy.Integer)
+                isinstance(val.lower, sym_int_types)
+                and isinstance(val.upper, sym_int_types)
                 and not val.is_bool
             ):
                 contains_symbolic_ints = True
diff --git a/test/python/fx_importer/basic_test.py b/test/python/fx_importer/basic_test.py
index 5c2ee65a3..be2235ec8 100644
--- a/test/python/fx_importer/basic_test.py
+++ b/test/python/fx_importer/basic_test.py
@@ -88,12 +88,13 @@ def test_import_frozen_exported_program_with_func_name():
 
 @run
 # CHECK-LABEL: test_import_frozen_exported_program_with_dynamic_shapes
-# CHECK:     func.func @test_net(%[[ARG0:[a-zA-Z0-9]+]]: !torch.vtensor<[?,4],f32>) -> !torch.vtensor<[?,4],f32>
+# CHECK:     func.func @test_net(%[[ARG0:[a-zA-Z0-9]+]]: !torch.vtensor<[?,?,5],f32>) -> !torch.vtensor<[?,?,5],f32>
 # CHECK:     %[[S0:.*]] = torch.symbolic_int "s0" {min_val = {{[0-9]+}}, max_val = {{[0-9]+}}} : !torch.int
-# CHECK:     torch.bind_symbolic_shape %[[ARG0]], [%[[S0]]], affine_map<()[s0] -> (s0, 4)> : !torch.vtensor<[?,4],f32>
-# CHECK:     %[[TANH:.*]] = torch.aten.tanh %[[ARG0]] : !torch.vtensor<[?,4],f32> -> !torch.vtensor<[?,4],f32>
-# CHECK:     torch.bind_symbolic_shape %[[TANH]], [%[[S0]]], affine_map<()[s0] -> (s0, 4)> : !torch.vtensor<[?,4],f32>
-# CHECK:     return %[[TANH]] : !torch.vtensor<[?,4],f32>
+# CHECK:     %[[S1:.*]] = torch.symbolic_int "s1" {min_val = 2, max_val = {{[0-9]+}}} : !torch.int
+# CHECK:     torch.bind_symbolic_shape %[[ARG0]], [%[[S0]], %[[S1]]], affine_map<()[s0, s1] -> (s0, s1, 5)> : !torch.vtensor<[?,?,5],f32>
+# CHECK:     %[[TANH:.*]] = torch.aten.tanh %[[ARG0]] : !torch.vtensor<[?,?,5],f32> -> !torch.vtensor<[?,?,5],f32>
+# CHECK:     torch.bind_symbolic_shape %[[TANH]], [%[[S0]], %[[S1]]], affine_map<()[s0, s1] -> (s0, s1, 5)> : !torch.vtensor<[?,?,5],f32>
+# CHECK:     return %[[TANH]] : !torch.vtensor<[?,?,5],f32>
 def test_import_frozen_exported_program_with_dynamic_shapes():
     class Basic(nn.Module):
         def __init__(self):
@@ -103,10 +104,11 @@ def test_import_frozen_exported_program_with_dynamic_shapes():
             return torch.tanh(x)
 
     batch = Dim("batch", max=10)
-    dynamic_shapes = {"x": {0: batch}}
+    channel = Dim("channel", min=2)
+    dynamic_shapes = {"x": {0: batch, 1: channel}}
     m = fx.export_and_import(
         Basic(),
-        torch.randn(3, 4),
+        torch.randn(3, 4, 5),
         dynamic_shapes=dynamic_shapes,
         func_name="test_net",
         import_symbolic_shape_expressions=True,