diff --git a/TrainingExtensions/torch/src/python/aimet_torch/quantsim.py b/TrainingExtensions/torch/src/python/aimet_torch/quantsim.py
index c637adfb6d..e398825c73 100644
--- a/TrainingExtensions/torch/src/python/aimet_torch/quantsim.py
+++ b/TrainingExtensions/torch/src/python/aimet_torch/quantsim.py
@@ -1799,6 +1799,28 @@ def _validate_torchquantizer(quant_sim_model):
             _validate_torchquantizer(quant_sim_model)
             OnnxSaver._export_model_to_onnx(quant_sim_model, dummy_input, model_path, is_conditional, onnx_export_args) # pylint: disable=protected-access
 
+    def _enable_output_quantizers_for_specific_cast_ops(self, inout_tensors_dtypes: Dict[torch.nn.Module, Tuple[torch.dtype, torch.dtype]]):
+        """
+        Enable output quantizer for Cast Ops where datatype of input tensor is int/bool
+        and data type of output tensor is float.
+        """
+        # pylint: disable=protected-access
+        model_prefix = self.connected_graph._model_name + '.'
+        torch_int_dtypes = {torch.int8, torch.int16, torch.int32, torch.int64, torch.bool, torch.uint8}
+        torch_float_dtypes = {torch.float16, torch.float32, torch.float64}
+
+        for module, inout_dtypes in inout_tensors_dtypes.items():
+            input_tensor_dtype = inout_dtypes[0]
+            output_tensor_dtype = inout_dtypes[1]
+            # pylint: disable=protected-access
+            module_name = self.connected_graph._module_to_name[module].split(model_prefix)[-1]
+
+            if input_tensor_dtype != output_tensor_dtype and input_tensor_dtype in torch_int_dtypes and output_tensor_dtype in torch_float_dtypes:
+                logger.info("Enabling output quantizer for module %s", module_name)
+                wrapped_module = getattr(self.model, module_name)
+                for output_quantizer in wrapped_module.output_quantizers:
+                    setattr(output_quantizer, 'enabled', True)
+
 
 def save_checkpoint(quant_sim_model: QuantizationSimModel, file_path: str):
     """
diff --git a/TrainingExtensions/torch/src/python/aimet_torch/utils.py b/TrainingExtensions/torch/src/python/aimet_torch/utils.py
index c5889e0d1d..8f96b111d2 100644
--- a/TrainingExtensions/torch/src/python/aimet_torch/utils.py
+++ b/TrainingExtensions/torch/src/python/aimet_torch/utils.py
@@ -53,6 +53,7 @@
 from aimet_common.defs import QuantScheme, QuantizationDataType, MAP_QUANT_SCHEME_TO_PYMO
 from aimet_common.utils import AimetLogger, Handle, log_with_error_and_assert_if_false
 import aimet_common.libpymo as libpymo
+from aimet_torch import elementwise_ops
 
 
 logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.Utils)
@@ -1011,3 +1012,35 @@ def fn(_, inputs):
         handle.remove()
 
     return cached_data
+
+
+def get_inout_tensors_dtypes_for_cast_modules(model: torch.nn.Module, input_tensor: Union[torch.Tensor, Tuple[torch.Tensor]]) -> Dict:
+    """
+    Get the datatype of input and output tensor of Cast modules in a Pytorch Model.
+
+    :param model: Pytorch Model
+    :param input_tensor: Input tensor to run forward pass for the model.
+                         A tuple of tensors should be passed if model has multiple inputs
+    :return: map of module -> (data type of input tensor, data type of output tensor)
+    """
+    inout_dtypes_map = {}
+
+    def record_dtypes(module, inputs, outputs):
+
+        # pylint: disable=protected-access
+        if isinstance(module, elementwise_ops.Cast):
+            input_dtype = None
+
+            if isinstance(inputs, (list, tuple)):
+                input_dtype = inputs[0].dtype
+
+            elif isinstance(inputs, torch.Tensor):
+                input_dtype = inputs.dtype
+
+            else:
+                raise ValueError
+
+            inout_dtypes_map[module] = (input_dtype, outputs.dtype)
+
+    run_hook_for_layers_with_given_input(model, input_tensor, record_dtypes)
+    return inout_dtypes_map