diff --git a/python/tvm/relay/frontend/tflite.py b/python/tvm/relay/frontend/tflite.py
index 200352c57fd2..fd56ff5f04c4 100644
--- a/python/tvm/relay/frontend/tflite.py
+++ b/python/tvm/relay/frontend/tflite.py
@@ -2763,7 +2763,12 @@ def convert_quantize(self, op):
         assert len(input_tensors) == 1, "input tensors length should be 1"
         input_tensor = input_tensors[0]
         input_tensor_type_str = self.get_tensor_type_str(input_tensor.tensor.Type())
-        in_expr = self.get_expr(input_tensor.tensor_idx)
+
+        if self.has_expr(input_tensor.tensor_idx):
+            in_expr = self.get_expr(input_tensor.tensor_idx)
+        else:
+            in_value = self.get_tensor_value(input_tensor)
+            in_expr = self.exp_tab.new_const(in_value, dtype=self.get_tensor_type_str(input_tensor.tensor.Type()))
 
         output_tensors = self.get_output_tensors(op)
         assert len(output_tensors) == 1, "output tensors length should be 1"
diff --git a/tests/python/frontend/tflite/test_forward.py b/tests/python/frontend/tflite/test_forward.py
index 4d7fc06c7fba..656e8aff3988 100644
--- a/tests/python/frontend/tflite/test_forward.py
+++ b/tests/python/frontend/tflite/test_forward.py
@@ -1888,7 +1888,7 @@ def _test_quantize_dequantize(data):
     # First TFLite quantize op converts float32 tensor to int8 tensor - Qnn quantize.
     # Second TFLite quantize op converts int8 tensor to int8 tensor - Qnn requantize.
     data_in = tf.keras.layers.Input(shape=data.shape[1:])
-    relu = tf.keras.layers.ReLU()(data_in)
+    relu = tf.keras.layers.ReLU()(data)
     add = tf.keras.layers.Add()([data_in, relu])
     concat = tf.keras.layers.Concatenate(axis=0)([relu, add])
     keras_model = tf.keras.models.Model(inputs=data_in, outputs=concat)