created original copy of the model by loading from disk

prototype_source/fx_graph_mode_ptq_dynamic.py

@@ -239,9 +239,27 @@ 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)
+
+
+model_to_quantize = LSTMModel(
+    ntoken = ntokens,
+    ninp = 512,
+    nhid = 256,
+    nlayers = 5,
+)
+
+model_to_quantize.load_state_dict(
+    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)
@@ -289,4 +307,4 @@ def time_model_evaluation(model, test_data):
 # 3. Conclusion
 # -------------
 # This tutorial introduces the api for post training dynamic quantization in FX Graph Mode,
-# which dynamically quantizes the same modules as Eager Mode Quantization.
+# which dynamically quantizes the same modules as Eager Mode Quantization.