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diff --git a/python/openvino/runtime/python_demos/OpenVINO_classification_sample_async/classification_sample_async.py b/python/openvino/runtime/python_demos/OpenVINO_classification_sample_async/classification_sample_async.py
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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# Copyright (C) 2018-2022 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+import logging as log
+import os
+import sys
+import warnings
+
+import cv2
+import numpy as np
+from openvino.preprocess import PrePostProcessor
+from openvino.runtime import AsyncInferQueue, Core, InferRequest, Layout, Type
+
+
+def parse_args() -> argparse.Namespace:
+    """Parse and return command line arguments."""
+    parser = argparse.ArgumentParser(add_help=False)
+    args = parser.add_argument_group('Options')
+    # fmt: off
+    args.add_argument('-h', '--help', action='help',
+                      help='Show this help message and exit.')
+    args.add_argument('-m', '--model', type=str, required=True,
+                      help='Required. Path to an .xml or .onnx file with a trained model.')
+    args.add_argument('-i', '--input', type=str, required=True, nargs='+',
+                      help='Required. Path to an image file(s).')
+    args.add_argument('-d', '--device', type=str, default='CPU',
+                      help='Optional. Specify the target device to infer on; CPU, GPU, MYRIAD, HDDL or HETERO: '
+                      'is acceptable. The sample will look for a suitable plugin for device specified. '
+                      'Default value is CPU.')
+    # fmt: on
+    return parser.parse_args()
+
+
+def completion_callback(infer_request: InferRequest, image_path: str) -> None:
+    predictions = next(iter(infer_request.results.values()))
+
+    # Change a shape of a numpy.ndarray with results to get another one with one dimension
+    probs = predictions.reshape(-1)
+
+    # Get an array of 10 class IDs in descending order of probability
+    top_10 = np.argsort(probs)[-10:][::-1]
+
+    header = 'class_id probability'
+
+    log.info(f'Image path: {image_path}')
+    log.info('Top 10 results: ')
+    log.info(header)
+    log.info('-' * len(header))
+
+    for class_id in top_10:
+        probability_indent = ' ' * (len('class_id') - len(str(class_id)) + 1)
+        log.info(f'{class_id}{probability_indent}{probs[class_id]:.7f}')
+
+    log.info('')
+
+
+def main() -> int:
+    log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
+    args = parse_args()
+
+# --------------------------- Step 1. Initialize OpenVINO Runtime Core ------------------------------------------------
+    log.info('Creating OpenVINO Runtime Core')
+    dla_plugins = os.environ.get('DLA_PLUGINS', default='')
+    if dla_plugins == '':
+        # Backwards compatability for old DLA_PLUGINS_XML_FILE
+        warnings.warn("DLA_PLUGINS_XML_FILE option is deprecated as of 2024.1, Please use DLA_PLUGINS") 
+        dla_plugins = os.environ.get('DLA_PLUGINS_XML_FILE', default='')
+    core = Core(dla_plugins)
+    if "FPGA" in args.device:
+        dla_arch_file = os.environ.get('DLA_ARCH_FILE')
+        if dla_arch_file is None:
+            raise Exception(f"To use FPGA, you need to specify the path to an arch_file!")
+        core.set_property(device_name="FPGA", properties={"ARCH_PATH": dla_arch_file})
+
+# --------------------------- Step 2. Read a model --------------------------------------------------------------------
+    log.info(f'Reading the model: {args.model}')
+    # (.xml and .bin files) or (.onnx file)
+    model = core.read_model(args.model)
+
+    if len(model.inputs) != 1:
+        log.error('Sample supports only single input topologies')
+        return -1
+
+    if len(model.outputs) != 1:
+        log.error('Sample supports only single output topologies')
+        return -1
+
+# --------------------------- Step 3. Set up input --------------------------------------------------------------------
+    # Read input images
+    images = [cv2.imread(image_path) for image_path in args.input]
+
+    # Resize images to model input dims
+    # Assuming we always have w=h, we will 
+    # figure out the layout from the dimensions
+    # start with the assumption of NHWC (TF)
+    _, h, w, c = model.input().shape
+
+    if h != w:
+        c = h
+        h = w
+
+    resized_images = [cv2.resize(image, (w, h)) for image in images]
+
+    # Add N dimension
+    input_tensors = [np.expand_dims(image, 0) for image in resized_images]
+
+    # Transpose from NHWC to NCHW
+    input_tensors = [np.transpose(tensor, (0, 3, 1, 2)) for tensor in input_tensors]
+
+# --------------------------- Step 4. Apply preprocessing -------------------------------------------------------------
+    ppp = PrePostProcessor(model)
+
+    # 1) Set input tensor information:
+    # - input() provides information about a single model input
+    # - layout of data is 'NCHW'
+    ppp.input().tensor().set_layout(Layout('NCHW'))  # noqa: N400
+
+    # 2) Here we suppose model has 'NCHW' layout for input
+    # DLA --> We let the demo select the layout based on the model
+    # ppp.input().model().set_layout(Layout('NCHW'))
+
+    # 3) Set output tensor information:
+    # - precision of tensor is supposed to be 'f32'
+    ppp.output().tensor().set_element_type(Type.f32)
+
+    # 4) Apply preprocessing modifing the original 'model'
+    model = ppp.build()
+
+# --------------------------- Step 5. Loading model to the device -----------------------------------------------------
+    log.info('Loading the model to the plugin')
+    compiled_model = core.compile_model(model, args.device)
+
+# --------------------------- Step 6. Create infer request queue ------------------------------------------------------
+    log.info('Starting inference in asynchronous mode')
+    # create async queue with optimal number of infer requests
+    infer_queue = AsyncInferQueue(compiled_model)
+    infer_queue.set_callback(completion_callback)
+
+# --------------------------- Step 7. Do inference --------------------------------------------------------------------
+    for i, input_tensor in enumerate(input_tensors):
+        infer_queue.start_async({0: input_tensor}, args.input[i])
+
+    infer_queue.wait_all()
+# ----------------------------------------------------------------------------------------------------------------------
+    log.info('This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool\n')
+    return 0
+
+
+if __name__ == '__main__':
+    sys.exit(main())