diff --git a/py/torch_tensorrt/dynamo/conversion/impl/select.py b/py/torch_tensorrt/dynamo/conversion/impl/select.py
index 8a5a0baead..6653e9e1a5 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/select.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/select.py
@@ -1,5 +1,5 @@
 import logging
-from typing import Optional, Sequence, Union, cast
+from typing import Optional, Sequence, Union
 
 import numpy as np
 import tensorrt as trt
@@ -21,7 +21,7 @@
     has_dynamic_shape,
     set_layer_name,
 )
-from torch_tensorrt.fx.types import Shape, TRTTensor
+from torch_tensorrt.fx.types import TRTTensor
 
 _LOGGER: logging.Logger = logging.getLogger(__name__)
 
@@ -32,8 +32,8 @@ def select(
     source_ir: Optional[SourceIR],
     name: str,
     input: TRTTensor,
-    dim: Shape,
-    index: Shape,
+    dim: int,
+    index: int,
 ) -> TRTTensor:
     if not isinstance(input, TRTTensor):
         raise RuntimeError(
@@ -42,19 +42,11 @@ def select(
         )
 
     ranks = len(input.shape)
-    dim = get_positive_dim(cast(int, dim), ranks)
-    dynamic_shape = has_dynamic_shape(input.shape)
-    if dynamic_shape:
-        # Check whether slice target dim is dynamic shape dim
-        assert input.shape[dim] != -1, "Can't select on negative shape dimension!"
-
-    if index >= input.shape[dim]:
-        raise RuntimeError(
-            f"cannot have index greater than the dimension length! {input.shape[dim]}"
-        )
+    dim = get_positive_dim(dim, ranks)
 
-    index_value = np.array(index, dtype=np.int32)
-    indices_tensor = ctx.net.add_constant(index_value.shape, index_value).get_output(0)
+    indices_tensor = get_trt_tensor(
+        ctx, np.array(index, dtype=np.int32), f"{name}_indices_tensor"
+    )
     layer = ctx.net.add_gather(input, indices_tensor, dim)
 
     return layer.get_output(0)
diff --git a/tests/py/dynamo/conversion/test_select_aten.py b/tests/py/dynamo/conversion/test_select_aten.py
index 9beda81a42..cce3fa0a30 100644
--- a/tests/py/dynamo/conversion/test_select_aten.py
+++ b/tests/py/dynamo/conversion/test_select_aten.py
@@ -10,10 +10,10 @@
 class TestSelectConverterOne(DispatchTestCase):
     @parameterized.expand(
         [
-            ("select_dim_index", 1, 0),
+            ("dim_index", 1, 0),
         ]
     )
-    def test_select(self, _, dim, index):
+    def test_select_2d(self, _, dim, index):
         class select(nn.Module):
             def __init__(self):
                 super().__init__()
@@ -27,14 +27,12 @@ def forward(self, input):
             input,
         )
 
-
-class TestSelectConverterTwo(DispatchTestCase):
     @parameterized.expand(
         [
-            ("select_dim_index", 1, 0),
+            ("dim_index", 1, 0),
         ]
     )
-    def test_select(self, _, dim, index):
+    def test_select_4d(self, _, dim, index):
         class select(nn.Module):
             def __init__(self):
                 super().__init__()
@@ -48,36 +46,43 @@ def forward(self, input):
             input,
         )
 
-
-class TestSelectConverterDynamicShape(DispatchTestCase):
     @parameterized.expand(
         [
             (
-                "select_dim_index",
-                (1, 3, 3),
-                (2, 3, 3),
+                "partial_dynamic_static_dim",
+                (1, 1, 3),
+                (2, 2, 3),
                 (3, 3, 3),
-                torch.int32,
-                1,
+                torch.float,
+                2,
                 0,
             ),
             (
-                "select_dim_index",
+                "partial_dynamic_dynamic_dim",
                 (1, 1, 3),
                 (2, 2, 3),
                 (3, 3, 3),
                 torch.float,
-                2,
-                0,
+                1,
+                1,
             ),
             (
-                "select_dim_index",
-                (3, 1, 1),
-                (3, 2, 2),
+                "fully_dynamic",
+                (1, 1, 1),
+                (2, 2, 2),
                 (3, 3, 3),
                 torch.float,
-                0,
-                2,
+                1,
+                1,
+            ),
+            (
+                "fully_dynamic_neg_dim",
+                (1, 1, 1),
+                (2, 2, 2),
+                (3, 3, 3),
+                torch.float,
+                -1,
+                1,
             ),
         ]
     )