[Test][PTQ] Fix transform_fn for AA in calibrate.py (openvinotoolki…

…t#1786)

### Changes

AA `transform_fn` aligned with standard quantize `tranform_fn` 

### Reason for changes

To align AA and standard quantize in `calibrate.py`

### Related tickets



### Tests

tests/openvino/tools/calibrate.py

@@ -665,6 +665,18 @@ def maybe_reshape_model(model, dataset, subset_size, input_to_tensor_name):
 
 
 # pylint: disable=protected-access
+def get_transform_fn(model_evaluator: ModelEvaluator):
+    def transform_fn(data_item):
+        _, batch_annotation, batch_input, _ = data_item
+        filled_inputs, _, _ = model_evaluator._get_batch_input(batch_input, batch_annotation)
+        input_data = {}
+        for name, value in filled_inputs[0].items():
+            input_data[model_evaluator.launcher.input_to_tensor_name[name]] = value
+        return input_data
+
+    return transform_fn
+
+
 def quantize_model(xml_path, bin_path, accuracy_checker_config, quantization_impl, quantization_parameters):
     ov_model = ov.Core().read_model(model=xml_path, weights=bin_path)
     model_evaluator = create_model_evaluator(accuracy_checker_config)
@@ -680,14 +692,7 @@ def quantize_model(xml_path, bin_path, accuracy_checker_config, quantization_imp
         raise NotImplementedError()
     quantization_parameters["advanced_parameters"] = advanced_parameters
 
-    def transform_fn(data_item):
-        _, batch_annotation, batch_input, _ = data_item
-        filled_inputs, _, _ = model_evaluator._get_batch_input(batch_input, batch_annotation)
-        input_data = {}
-        for name, value in filled_inputs[0].items():
-            input_data[model_evaluator.launcher.input_to_tensor_name[name]] = value
-        return input_data
-
+    transform_fn = get_transform_fn(model_evaluator)
     calibration_dataset = nncf.Dataset(model_evaluator.dataset, transform_fn)
 
     if get_allow_reshape_input(accuracy_checker_config):
@@ -703,7 +708,6 @@ def transform_fn(data_item):
     return quantized_model
 
 
-# pylint: disable=protected-access
 def quantize_model_with_accuracy_control(
     xml_path: str, bin_path: str, accuracy_checker_config, quantization_impl: str, quantization_parameters
 ):
@@ -712,11 +716,7 @@ def quantize_model_with_accuracy_control(
     model_evaluator.load_network_from_ir([{"model": xml_path, "weights": bin_path}])
     model_evaluator.select_dataset("")
 
-    def transform_fn(data_item):
-        _, batch_annotation, batch_input, _ = data_item
-        filled_inputs, _, _ = model_evaluator._get_batch_input(batch_input, batch_annotation)
-        return filled_inputs[0]
-
+    transform_fn = get_transform_fn(model_evaluator)
     calibration_dataset = nncf.Dataset(model_evaluator.dataset, transform_fn)
     validation_dataset = nncf.Dataset(list(range(model_evaluator.dataset.full_size)))