diff --git a/python/tvm/testing/aot.py b/python/tvm/testing/aot.py
index 563a7dff4a50..30d3c78ae43b 100644
--- a/python/tvm/testing/aot.py
+++ b/python/tvm/testing/aot.py
@@ -24,7 +24,7 @@
 import subprocess
 import tarfile
 import logging
-from typing import Any, NamedTuple, Union, Optional, List, Dict
+from typing import Any, NamedTuple, Union, Tuple, Optional, List, Dict
 import numpy as np
 
 import tvm
@@ -901,6 +901,35 @@ def compile_and_run(
     )
 
 
+def get_dtype_range(dtype: str) -> Tuple[int, int]:
+    """
+    Produces the min,max for a give data type.
+
+    Parameters
+    ----------
+    dtype : str
+        a type string (e.g., int8, float64)
+
+    Returns
+    -------
+    type_info.min : int
+        the minimum of the range
+    type_info.max : int
+        the maximum of the range
+    """
+    type_info = None
+    np_dtype = np.dtype(dtype)
+    kind = np_dtype.kind
+
+    if kind == "f":
+        type_info = np.finfo(np_dtype)
+    elif kind in ["i", "u"]:
+        type_info = np.iinfo(np_dtype)
+    else:
+        raise TypeError(f"dtype ({dtype}) must indicate some floating-point or integral data type.")
+    return type_info.min, type_info.max
+
+
 def generate_ref_data(mod, input_data, params=None, target="llvm"):
     """Generate reference data through executing the relay module"""
     with tvm.transform.PassContext(opt_level=3, config={"tir.disable_vectorize": True}):
diff --git a/tests/python/contrib/test_cmsisnn/test_binary_ops.py b/tests/python/contrib/test_cmsisnn/test_binary_ops.py
index 29335072bf06..663a1bd45d5c 100644
--- a/tests/python/contrib/test_cmsisnn/test_binary_ops.py
+++ b/tests/python/contrib/test_cmsisnn/test_binary_ops.py
@@ -25,7 +25,7 @@
 import tvm
 from tvm import relay
 from tvm.relay.op.contrib import cmsisnn
-from tvm.testing.aot import generate_ref_data, AOTTestModel, compile_and_run
+from tvm.testing.aot import get_dtype_range, generate_ref_data, AOTTestModel, compile_and_run
 from tvm.micro.testing.aot_test_utils import (
     AOT_USMP_CORSTONE300_RUNNER,
 )
@@ -34,7 +34,6 @@
     skip_if_no_reference_system,
     make_module,
     make_qnn_relu,
-    get_range_for_dtype_str,
     assert_partitioned_function,
     assert_no_external_function,
     create_test_runner,
@@ -45,9 +44,8 @@ def generate_tensor_constant():
     rng = np.random.default_rng(12321)
     dtype = "int8"
     shape = (1, 16, 16, 3)
-    values = tvm.nd.array(
-        rng.integers(np.iinfo(dtype).min, high=np.iinfo(dtype).max, size=shape, dtype=dtype)
-    )
+    in_min, in_max = get_dtype_range(dtype)
+    values = tvm.nd.array(rng.integers(in_min, high=in_max, size=shape, dtype=dtype))
     return relay.const(values, dtype)
 
 
@@ -136,7 +134,7 @@ def test_op_int8(
     assert_partitioned_function(orig_mod, cmsisnn_mod)
 
     # validate the output
-    in_min, in_max = get_range_for_dtype_str(dtype)
+    in_min, in_max = get_dtype_range(dtype)
     inputs = {
         "input_0": np.random.randint(in_min, high=in_max, size=shape, dtype=dtype),
         "input_1": np.random.randint(in_min, high=in_max, size=shape, dtype=dtype),
@@ -196,7 +194,7 @@ def test_same_input_to_binary_op(op, relu_type):
     ), "Composite function for the binary op should have only 1 parameter."
 
     # validate the output
-    in_min, in_max = get_range_for_dtype_str(dtype)
+    in_min, in_max = get_dtype_range(dtype)
     inputs = {
         "input": np.random.randint(in_min, high=in_max, size=shape, dtype=dtype),
     }
@@ -275,7 +273,7 @@ def test_constant_input_int8(op, input_0, input_1):
     assert_partitioned_function(orig_mod, cmsisnn_mod)
 
     # validate the output
-    in_min, in_max = get_range_for_dtype_str(dtype)
+    in_min, in_max = get_dtype_range(dtype)
     inputs = {}
     if isinstance(input_0, tvm.relay.expr.Var):
         inputs.update({"input_0": np.random.randint(in_min, high=in_max, size=shape, dtype=dtype)})
diff --git a/tests/python/contrib/test_cmsisnn/test_conv2d.py b/tests/python/contrib/test_cmsisnn/test_conv2d.py
index 66ff5d793880..20e7b9ed2f62 100644
--- a/tests/python/contrib/test_cmsisnn/test_conv2d.py
+++ b/tests/python/contrib/test_cmsisnn/test_conv2d.py
@@ -24,6 +24,7 @@
 from tvm.relay.op.contrib import cmsisnn
 
 from tvm.testing.aot import (
+    get_dtype_range,
     generate_ref_data,
     AOTTestModel,
     compile_models,
@@ -33,7 +34,6 @@
 from tvm.micro.testing.aot_test_utils import AOT_USMP_CORSTONE300_RUNNER
 from .utils import (
     make_module,
-    get_range_for_dtype_str,
     get_same_padding,
     get_conv2d_qnn_params,
     get_kernel_bias_dtype,
@@ -82,10 +82,11 @@ def make_model(
         p = get_same_padding((shape[1], shape[2]), (kernel_h, kernel_w), dilation, strides)
 
     rng = np.random.default_rng(12321)
+    kmin, kmax = get_dtype_range(kernel_dtype)
     kernel = tvm.nd.array(
         rng.integers(
-            np.iinfo(kernel_dtype).min,
-            high=np.iinfo(kernel_dtype).max,
+            kmin,
+            high=kmax,
             size=kernel_shape,
             dtype=kernel_dtype,
         )
@@ -157,7 +158,7 @@ def test_conv2d_number_primfunc_args(
     kernel_w = kernel_size[1]
     kernel_shape = (kernel_h, kernel_w, ifm_shape[3] // groups, out_channels)
     kernel_zero_point = 0
-    in_min, in_max = get_range_for_dtype_str(dtype)
+    in_min, in_max = get_dtype_range(dtype)
     relu_type = "RELU"
 
     kernel_dtype, bias_dtype = get_kernel_bias_dtype(dtype)
@@ -264,7 +265,7 @@ def test_conv2d_symmetric_padding(
     kernel_w = kernel_size[1]
     kernel_shape = (kernel_h, kernel_w, ifm_shape[3] // groups, out_channels)
     kernel_zero_point = 0
-    in_min, in_max = get_range_for_dtype_str(dtype)
+    in_min, in_max = get_dtype_range(dtype)
 
     kernel_dtype, bias_dtype = get_kernel_bias_dtype(dtype)
 
@@ -358,7 +359,7 @@ def test_conv2d_asymmetric_padding(
     kernel_w = kernel_size[1]
     kernel_shape = (kernel_h, kernel_w, ifm_shape[3] // groups, out_channels)
     kernel_zero_point = 0
-    in_min, in_max = get_range_for_dtype_str(dtype)
+    in_min, in_max = get_dtype_range(dtype)
 
     kernel_dtype, bias_dtype = get_kernel_bias_dtype(dtype)
 
@@ -454,7 +455,7 @@ def test_pad_conv2d_fusion_int8(
     kernel_w = kernel_size[1]
     kernel_shape = (kernel_h, kernel_w, ifm_shape[3] // groups, out_channels)
     kernel_zero_point = 0
-    in_min, in_max = get_range_for_dtype_str(dtype)
+    in_min, in_max = get_dtype_range(dtype)
 
     kernel_dtype, bias_dtype = get_kernel_bias_dtype(dtype)
     output_scale, output_zero_point = get_conv2d_qnn_params(
@@ -567,7 +568,7 @@ def test_invalid_pad_conv2d_fusion_int8(
     kernel_w = kernel_size[1]
     kernel_shape = (kernel_h, kernel_w, ifm_shape[3] // groups, out_channels)
     kernel_zero_point = 0
-    in_min, in_max = get_range_for_dtype_str(dtype)
+    in_min, in_max = get_dtype_range(dtype)
 
     kernel_dtype, bias_dtype = get_kernel_bias_dtype(dtype)
 
@@ -740,7 +741,7 @@ def test_depthwise(
     kernel_w = kernel_size[1]
     kernel_shape = (kernel_h, kernel_w, ifm_shape[3] // groups, out_channels)
     kernel_zero_point = 0
-    in_min, in_max = get_range_for_dtype_str(dtype)
+    in_min, in_max = get_dtype_range(dtype)
 
     groups = ifm_shape[3]
     kernel_layout = "HWOI"
@@ -844,7 +845,7 @@ def test_relay_conv2d_cmsisnn_depthwise_int8(
     test_runner = AOT_USMP_CORSTONE300_RUNNER
 
     dtype = "int8"
-    in_min, in_max = get_range_for_dtype_str(dtype)
+    in_min, in_max = get_dtype_range(dtype)
 
     ifm_shape = (1, 24, 24, 1)
     groups = ifm_shape[3]
diff --git a/tests/python/contrib/test_cmsisnn/test_fully_connected.py b/tests/python/contrib/test_cmsisnn/test_fully_connected.py
index 3b220eb42c9b..46b1488eb3fe 100644
--- a/tests/python/contrib/test_cmsisnn/test_fully_connected.py
+++ b/tests/python/contrib/test_cmsisnn/test_fully_connected.py
@@ -23,10 +23,9 @@
 from tvm import relay
 from tvm.relay.op.contrib import cmsisnn
 
-from tvm.testing.aot import generate_ref_data, AOTTestModel, compile_and_run
+from tvm.testing.aot import get_dtype_range, generate_ref_data, AOTTestModel, compile_and_run
 from .utils import (
     make_module,
-    get_range_for_dtype_str,
     get_conv2d_qnn_params,
     make_qnn_relu,
     assert_partitioned_function,
@@ -55,10 +54,11 @@ def make_model(
     """Return a model and any parameters it may have"""
     input_ = relay.var("input", shape=in_shape, dtype=dtype)
     rng = np.random.default_rng(12321)
+    kmin, kmax = get_dtype_range(kernel_dtype)
     weight = tvm.nd.array(
         rng.integers(
-            np.iinfo(kernel_dtype).min,
-            high=np.iinfo(kernel_dtype).max,
+            kmin,
+            high=kmax,
             size=kernel_shape,
             dtype=kernel_dtype,
         )
@@ -123,7 +123,7 @@ def test_ops(
     kernel_zero_point = 0
     kernel_shape = [out_channels, in_shape[1]]
     conv2d_kernel_shape = (1, 1, kernel_shape[0], kernel_shape[1])
-    in_min, in_max = get_range_for_dtype_str(dtype)
+    in_min, in_max = get_dtype_range(dtype)
 
     output_scale, output_zero_point = get_conv2d_qnn_params(
         conv2d_kernel_shape,
diff --git a/tests/python/contrib/test_cmsisnn/test_fuse_pads.py b/tests/python/contrib/test_cmsisnn/test_fuse_pads.py
index f57dc5cd5bab..4ea306cc4382 100644
--- a/tests/python/contrib/test_cmsisnn/test_fuse_pads.py
+++ b/tests/python/contrib/test_cmsisnn/test_fuse_pads.py
@@ -19,7 +19,7 @@
 import numpy as np
 import pytest
 import tvm
-import tvm.testing
+from tvm.testing.aot import get_dtype_range
 from tvm import relay
 from .utils import CheckForPadsWithinCompositeFunc
 
@@ -59,10 +59,11 @@ def test_invalid_padding_for_fusion(ifm_shape, pad_width, conv2d_padding, ofm_sh
         pad_mode="constant",
     )
     rng = np.random.default_rng(12321)
+    in_min, in_max = get_dtype_range(dtype)
     local_weight = tvm.nd.array(
         rng.integers(
-            np.iinfo(dtype).min,
-            high=np.iinfo(dtype).max,
+            in_min,
+            high=in_max,
             size=(ofm_channels, kernel_size[0], kernel_size[1], ifm_shape[3]),
             dtype=dtype,
         )
@@ -139,10 +140,11 @@ def test_pad_conv2d_fusion_noncmsisnn_target(ifm_shape, pad_width, conv2d_paddin
         pad_mode="constant",
     )
     rng = np.random.default_rng(12321)
+    in_min, in_max = get_dtype_range(dtype)
     local_weight = tvm.nd.array(
         rng.integers(
-            np.iinfo(dtype).min,
-            high=np.iinfo(dtype).max,
+            in_min,
+            high=in_max,
             size=(ofm_channels, kernel_size[0], kernel_size[1], ifm_shape[3]),
             dtype=dtype,
         )
@@ -217,10 +219,11 @@ def test_pad_conv2d_fusion(ifm_shape, pad_width, conv2d_padding, ofm_shape):
         pad_mode="constant",
     )
     rng = np.random.default_rng(12321)
+    kmin, kmax = get_dtype_range(dtype)
     local_weight = tvm.nd.array(
         rng.integers(
-            np.iinfo(dtype).min,
-            high=np.iinfo(dtype).max,
+            kmin,
+            high=kmax,
             size=(ofm_channels, kernel_size[0], kernel_size[1], ifm_shape[3]),
             dtype=dtype,
         )
@@ -281,10 +284,11 @@ def test_without_preceding_pad():
     ofm_shape = (1, 56, 56, 64)
     local_input = relay.var("local_input", shape=ifm_shape, dtype=dtype)
     rng = np.random.default_rng(12321)
+    kmin, kmax = get_dtype_range(dtype)
     local_weight = tvm.nd.array(
         rng.integers(
-            np.iinfo(dtype).min,
-            high=np.iinfo(dtype).max,
+            kmin,
+            high=kmax,
             size=(64, 3, 3, 64),
             dtype=dtype,
         )
diff --git a/tests/python/contrib/test_cmsisnn/test_generate_constants.py b/tests/python/contrib/test_cmsisnn/test_generate_constants.py
index 86737370bc5d..b83884128441 100644
--- a/tests/python/contrib/test_cmsisnn/test_generate_constants.py
+++ b/tests/python/contrib/test_cmsisnn/test_generate_constants.py
@@ -20,7 +20,7 @@
 import numpy as np
 import pytest
 import tvm
-import tvm.testing
+from tvm.testing.aot import get_dtype_range
 from tvm import relay
 from tvm.relay.op.contrib import cmsisnn
 
@@ -107,10 +107,11 @@ def make_model(
 
     weight_shape = (kernel_h, kernel_w, shape[3] // groups, out_channels)
     rng = np.random.default_rng(12321)
+    kmin, kmax = get_dtype_range(kernel_dtype)
     weight = tvm.nd.array(
         rng.integers(
-            np.iinfo(kernel_dtype).min,
-            high=np.iinfo(kernel_dtype).max,
+            kmin,
+            high=kmax,
             size=weight_shape,
             dtype=kernel_dtype,
         )
diff --git a/tests/python/contrib/test_cmsisnn/test_invalid_graphs.py b/tests/python/contrib/test_cmsisnn/test_invalid_graphs.py
index c66f9d0e0726..ace1db7811da 100644
--- a/tests/python/contrib/test_cmsisnn/test_invalid_graphs.py
+++ b/tests/python/contrib/test_cmsisnn/test_invalid_graphs.py
@@ -19,13 +19,12 @@
 import numpy as np
 import tvm
 
-from tvm.testing.aot import AOTTestModel, compile_and_run, generate_ref_data
+from tvm.testing.aot import AOTTestModel, get_dtype_range, compile_and_run, generate_ref_data
 from tvm.micro.testing.aot_test_utils import (
     AOT_USMP_CORSTONE300_RUNNER,
 )
 from .utils import (
     skip_if_no_reference_system,
-    get_range_for_dtype_str,
 )
 
 
@@ -58,7 +57,7 @@ def @main(%data : Tensor[(16, 29), int8]) -> Tensor[(16, 29), int8] {
     use_unpacked_api = True
     test_runner = AOT_USMP_CORSTONE300_RUNNER
     dtype = "int8"
-    in_min, in_max = get_range_for_dtype_str(dtype)
+    in_min, in_max = get_dtype_range(dtype)
     rng = np.random.default_rng(12345)
     inputs = {"data": rng.integers(in_min, high=in_max, size=(16, 29), dtype=dtype)}
     outputs = generate_ref_data(orig_mod["main"], inputs, params)
diff --git a/tests/python/contrib/test_cmsisnn/test_networks.py b/tests/python/contrib/test_cmsisnn/test_networks.py
index 6f9f3743a622..9f64be246182 100644
--- a/tests/python/contrib/test_cmsisnn/test_networks.py
+++ b/tests/python/contrib/test_cmsisnn/test_networks.py
@@ -24,12 +24,12 @@
 from tvm import relay
 from tvm.contrib.download import download_testdata
 from tvm.relay.op.contrib import cmsisnn
-from tvm.testing.aot import AOTTestModel, compile_and_run, generate_ref_data
+from tvm.testing.aot import AOTTestModel, get_dtype_range, compile_and_run, generate_ref_data
 from tvm.micro.testing.aot_test_utils import (
     AOT_CORSTONE300_RUNNER,
     AOT_USMP_CORSTONE300_RUNNER,
 )
-from .utils import skip_if_no_reference_system, get_range_for_dtype_str
+from .utils import skip_if_no_reference_system
 
 # pylint: disable=import-outside-toplevel
 def _convert_to_relay(
@@ -93,7 +93,7 @@ def test_cnn_small(test_runner):
 
     input_shape = (1, 490)
     dtype = "int8"
-    in_min, in_max = get_range_for_dtype_str(dtype)
+    in_min, in_max = get_dtype_range(dtype)
     rng = np.random.default_rng(12345)
     input_data = rng.integers(in_min, high=in_max, size=input_shape, dtype=dtype)
 
diff --git a/tests/python/contrib/test_cmsisnn/test_pooling.py b/tests/python/contrib/test_cmsisnn/test_pooling.py
index 7657e0e63220..c6e5f02e712a 100644
--- a/tests/python/contrib/test_cmsisnn/test_pooling.py
+++ b/tests/python/contrib/test_cmsisnn/test_pooling.py
@@ -23,6 +23,7 @@
 from tvm.relay.op.contrib import cmsisnn
 
 from tvm.testing.aot import (
+    get_dtype_range,
     generate_ref_data,
     AOTTestModel,
     compile_and_run,
@@ -30,7 +31,6 @@
 from tvm.micro.testing.aot_test_utils import AOT_USMP_CORSTONE300_RUNNER
 from .utils import (
     make_module,
-    get_range_for_dtype_str,
     get_same_padding,
     make_qnn_relu,
     assert_partitioned_function,
@@ -128,7 +128,7 @@ def test_ops(
     assert_partitioned_function(orig_mod, cmsisnn_mod)
 
     # validate the output
-    in_min, in_max = get_range_for_dtype_str(dtype)
+    in_min, in_max = get_dtype_range(dtype)
     np.random.seed(0)
     inputs = {
         "input": np.random.randint(in_min, high=in_max, size=in_shape, dtype=dtype),
diff --git a/tests/python/contrib/test_cmsisnn/test_remove_reshapes.py b/tests/python/contrib/test_cmsisnn/test_remove_reshapes.py
index 8b33a8a90b76..3cd60341ebfe 100644
--- a/tests/python/contrib/test_cmsisnn/test_remove_reshapes.py
+++ b/tests/python/contrib/test_cmsisnn/test_remove_reshapes.py
@@ -23,6 +23,7 @@
 from tvm.relay.op.contrib import cmsisnn
 
 from tvm.testing.aot import (
+    get_dtype_range,
     generate_ref_data,
     AOTTestModel,
     compile_models,
@@ -31,7 +32,6 @@
 from tvm.micro.testing.aot_test_utils import AOT_USMP_CORSTONE300_RUNNER
 from .utils import (
     make_module,
-    get_range_for_dtype_str,
     get_same_padding,
     make_qnn_relu,
     assert_partitioned_function,
@@ -126,7 +126,7 @@ def test_reshape_removal(padding):
 
     # generate reference output
     rng = np.random.default_rng(12345)
-    in_min, in_max = get_range_for_dtype_str("int8")
+    in_min, in_max = get_dtype_range("int8")
     inputs = {"input": rng.integers(in_min, high=in_max, size=in_shape, dtype="int8")}
     output_list = generate_ref_data(orig_mod["main"], inputs, params=None)
 
diff --git a/tests/python/contrib/test_cmsisnn/test_softmax.py b/tests/python/contrib/test_cmsisnn/test_softmax.py
index d048723529e0..0316d567adf4 100644
--- a/tests/python/contrib/test_cmsisnn/test_softmax.py
+++ b/tests/python/contrib/test_cmsisnn/test_softmax.py
@@ -24,12 +24,11 @@
 import tvm.testing
 from tvm import relay
 from tvm.relay.op.contrib import cmsisnn
-from tvm.testing.aot import AOTTestModel, compile_and_run, generate_ref_data
+from tvm.testing.aot import get_dtype_range, AOTTestModel, compile_and_run, generate_ref_data
 
 from .utils import (
     skip_if_no_reference_system,
     make_module,
-    get_range_for_dtype_str,
     assert_partitioned_function,
     assert_no_external_function,
     create_test_runner,
@@ -78,7 +77,7 @@ def test_op_int8(zero_point, scale, compiler_cpu, cpu_flags):
     assert_partitioned_function(orig_mod, cmsisnn_mod)
 
     # validate the output
-    in_min, in_max = get_range_for_dtype_str(dtype)
+    in_min, in_max = get_dtype_range(dtype)
     np.random.seed(0)
     input_data = np.random.randint(in_min, high=in_max, size=shape, dtype=dtype)
     inputs = {"in0": input_data}
diff --git a/tests/python/contrib/test_cmsisnn/utils.py b/tests/python/contrib/test_cmsisnn/utils.py
index f3a6b0c1343b..1ec3e609f1a3 100644
--- a/tests/python/contrib/test_cmsisnn/utils.py
+++ b/tests/python/contrib/test_cmsisnn/utils.py
@@ -23,7 +23,7 @@
 
 import tvm
 from tvm import relay
-from tvm.testing.aot import AOTTestRunner
+from tvm.testing.aot import AOTTestRunner, get_dtype_range
 
 
 def skip_if_no_reference_system(func):
@@ -86,30 +86,6 @@ def assert_no_external_function(mod):
     assert not any(attrs), "No function should have an external attribute."
 
 
-def get_range_for_dtype_str(dtype):
-    """
-    Produces the min,max for a give data type.
-
-    Parameters
-    ----------
-    dtype : str
-        a type string (e.g., int8)
-
-    Returns
-    -------
-    type_info.min : int
-        the minimum of the range
-    type_info.max : int
-        the maximum of the range
-    """
-
-    try:
-        type_info = np.iinfo(dtype)
-    except ValueError:
-        type_info = np.finfo(dtype)
-    return type_info.min, type_info.max
-
-
 def make_module(func):
     """Creates IRModule from Function"""
     func = relay.Function(relay.analysis.free_vars(func), func)
@@ -193,11 +169,11 @@ def get_conv2d_qnn_params(
     output_zp : int
         zero point of the output tensor
     """
-    input_dtype_min, input_dtype_max = get_range_for_dtype_str(input_dtype)
+    input_dtype_min, input_dtype_max = get_dtype_range(input_dtype)
     input_max = input_scale * (input_dtype_max - input_zp)
     input_min = input_scale * (input_dtype_min - input_zp)
 
-    kernel_dtype_min, kernel_dtype_max = get_range_for_dtype_str(kernel_dtype)
+    kernel_dtype_min, kernel_dtype_max = get_dtype_range(kernel_dtype)
     kernel_sc_max = np.max(kernel_scale)
     kernel_max = kernel_sc_max * (kernel_dtype_max - kernel_zp)
 
@@ -222,7 +198,7 @@ def get_conv2d_qnn_params(
 
     output_max = max(output_limits)
     output_min = min(output_limits)
-    output_dtype_min, output_dtype_max = get_range_for_dtype_str(output_dtype)
+    output_dtype_min, output_dtype_max = get_dtype_range(output_dtype)
 
     output_scale = (output_max - output_min) / (output_dtype_max - output_dtype_min)
     output_zp = int(output_dtype_min - (output_min / output_scale))
@@ -236,7 +212,7 @@ def make_qnn_relu(expr, fused_activation_fn, scale, zero_point, dtype):
 
     # Get min/max of the output dtype. This will be used to ensure that clip a_min/a_max are not
     # beyond the dtype range.
-    qmin, qmax = get_range_for_dtype_str(dtype)
+    qmin, qmax = get_dtype_range(dtype)
 
     # The input expr is a quantized tensor with its scale and zero point. We calculate the
     # suitable clip off points based on these scale and zero point.
diff --git a/tests/python/relay/aot/test_crt_forward_declarations.py b/tests/python/relay/aot/test_crt_forward_declarations.py
index 7454f85ed153..e54846f3aaca 100644
--- a/tests/python/relay/aot/test_crt_forward_declarations.py
+++ b/tests/python/relay/aot/test_crt_forward_declarations.py
@@ -34,30 +34,6 @@
 )
 
 
-def get_range_for_dtype_str(dtype):
-    """
-    Produces the min,max for a give data type.
-
-    Parameters
-    ----------
-    dtype : str
-        a type string (e.g., int8)
-
-    Returns
-    -------
-    type_info.min : int
-        the minimum of the range
-    type_info.max : int
-        the maximum of the range
-    """
-
-    try:
-        type_info = np.iinfo(dtype)
-    except ValueError:
-        type_info = np.finfo(dtype)
-    return type_info.min, type_info.max
-
-
 def _change_ndarray_layout(arr, src_layout, dst_layout):
     """Makes a copy of an ndarray, reshaping it to a new data layout.