created original copy of the model by loading from disk

prototype_source/fx_graph_mode_ptq_dynamic.py

@@ -239,9 +239,13 @@ def evaluate(model_, data_source):
     .set_object_type(nn.LSTM, default_dynamic_qconfig)
     .set_object_type(nn.Linear, default_dynamic_qconfig)
 )
-# Deepcopying the original model because quantization api changes the model inplace and we want
+# Load model to create the original model because quantization api changes the model inplace and we want
 # to keep the original model for future comparison
-model_to_quantize = copy.deepcopy(model)
+
+# Load the model
+model_to_quantize = torch.load(model_data_filepath + 'word_language_model_quantize.pth', map_location=torch.device('cpu'))
+model_to_quantize.eval()
+
 prepared_model = prepare_fx(model_to_quantize, qconfig_mapping, example_inputs)
 print("prepared model:", prepared_model)
 quantized_model = convert_fx(prepared_model)