# Windows ML

[Windows ML](https://learn.microsoft.com/en-us/windows/ai/new-windows-ml/api-reference?tabs=csharp) is part of the <cite>Microsoft.Windows.AI.MachineLearning</cite> package and serves as the AI inferencing core of Windows AI Foundry. It seamlessly runs AI workloads using built-in Windows AI APIs or Foundry Local models, enabling developers to leverage device capabilities for model inference. Windows ML automatically selects the best hardware, balancing the load and enhancing performance.

![../../_images/winml_workflow.png](data:image/png;base64,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)

WindowsML stack

The following tutorial uses Windows ML for the BYOM execution of the mobilenet\_v2.onnx model on a Qualcomm Snapdragon X and X2 platforms.

## Run ONNX models with Windows ML

This section demonstrates using Windows ML to accelerate the workloads on Snapdragon X and X2 platforms. You have the option to select the CPU, GPU, or NPU runtime to run the AI workloads.

### Prerequisites

- Python amd64 version 3.12.6
- Visual Studio Redistributable Version 14.44.35208 or above
- onnxruntime-windowsml python package

Follow the steps in the Setup section to automatically download and install the prerequisites.

### Setup

The tutorial requires the following setup which takes about five minutes to complete. For more information about the setup, see [winml_setup.ps1](https://raw.githubusercontent.com/quic/wos-ai/refs/heads/main/Scripts/winml_setup.ps1).

Open PowerShell in administrator mode and run the following commands to install the dependencies. You must run PowerShell in administrator mode to grant the elevated permissions required to automatically install the necessary applications and execute the PowerShell scripts.

1. Set `$DIR_PATH`. By default it is set to `C:\\WoS_AI` although you can change to your desired path.

$DIR_PATH  = "C:\WoS_AI"
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2. Run the following command to download the setup script.

if (!(Test-Path $DIR_PATH\Downloads\Setup_Scripts)) {mkdir $DIR_PATH\Downloads\Setup_Scripts}
        Invoke-WebRequest -O winml_setup.ps1 https://raw.githubusercontent.com/quic/wos-ai/refs/heads/main/Scripts/winml_setup.ps1
        Move-Item -Path ".\winml_setup.ps1" -Destination $DIR_PATH\Downloads\Setup_Scripts -Force
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3. If running PowerShell scripts is disabled on the system, run the following command and enter ‘A’(Yes for all).

Set-ExecutionPolicy RemoteSigned
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4. Run the following commands to install the dependencies for Windows ML.

    Windows ML setup:

cd $DIR_PATH
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powershell -command "&{. .\Downloads\Setup_Scripts\winml_setup.ps1; WinML_Setup -rootDirPath $DIR_PATH}"
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    The command downloads and installs the following dependencies:

> 
> 
> - Python amd64 version 3.12.6
>     - Model artifacts include mobilenet\_v2.onnx model and io\_utils.py for pre/post processing.
>     - Visual Studio Redistributable Version 14.44.35208 or above.
>     - Creates a virtual environment (SDX\_WinML\_ENV) with the necessary libraries for Windows ML.
>     - onnxruntime-windowsml

Note

    - Avoid installing any other ONNX runtime variant in this environment. The setup.ps1 script installs the necessary variant (onnxruntime-winml). Installing a different variant may result in fetching the incorrect ONNX runtime variant and could cause issues.
5. When the setup is complete, the following folder structure is in $DIR\_PATH:

![../../_images/folders_structure_ort.png](data:image/png;base64,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)
    - Downloads: This folder stores all the files required to complete the setup, such as Python installers, setup scripts, etc.
    - Python\_Env: This directory contains the virtual environment (SDX\_WinML\_ENV) created for the EPs.
    - Debug\_Logs: This directory contains logs corresponding to setup.
    - Models: This directory holds model sub-directories and its respective artifacts (Ex: Mobilenet\_v2).

### Run the model using JIT and AoT approaches

This section describes using Windows ML to run the classification model (Mobilenet\_v2.onnx) on the Qualcomm Snapdragon NPU. The following example illustrates the model execution on the NPU backend.

- Open a PowerShell and run the following commands to set `$DIR_PATH` and activate the Python virtual environment. Ensure that `$DIR_PATH` is the same as the one used during the setup.

> 
> 
> ${DIR_PATH} = "C:\WoS_AI"
>         Copy to clipboard
> 
> 
> powershell -NoExit -command "&{cd $DIR_PATH; . .\Downloads\Setup_Scripts\winml_setup.ps1; Activate_WinML_VENV -rootDirPath $DIR_PATH}"
>         Copy to clipboard

There are two approaches to run the model:

- Just-in-Time (JIT): Execute the model executed directly using WinML with online graph preparation.
- Ahead-of-Time (AoT): Run a cached model using the model’s context binary, thereby eliminating the model initialization time.

Tab JIT Approach
Tab AoT Approach

1. Run the following to create the winml\_JIT.py file in the working directory.

notepad.exe winml_JIT.py
        Copy to clipboard
2. Copy the following code into the *winml\_JIT.py* file and save.

# File_name : winml_JIT.py
        
        import onnxruntime as ort
        import numpy as np
        import os
        import time
        #Qualcomm utility for pre-/postprocessing of input/outputs in model inference
        import io_utils
        
        code_start_time = time.time()
        
        reg_winml_start_time = time.time()
        from pathlib import Path
        from importlib import metadata
        site_packages_path = Path(str(metadata.distribution('winrt-runtime').locate_file('')))
        dll_path = site_packages_path / 'winrt' / 'msvcp140.dll'
        if dll_path.exists():
           dll_path.unlink()
        
        from winui3.microsoft.windows.applicationmodel.dynamicdependency.bootstrap import (
           InitializeOptions,
           initialize
        )
        
        import winui3.microsoft.windows.ai.machinelearning as winml
        import onnxruntime as ort
        
        with initialize(options=InitializeOptions.ON_NO_MATCH_SHOW_UI):
           catalog = winml.ExecutionProviderCatalog.get_default()
           # Filter EPs that the app supports
           available_provider = catalog.find_all_providers()
           print(f"all available procider : ")
           for provider in available_provider:
              print(f"{provider.name}: {provider.ready_state}")
        
           possible_provider_list = ['QNNExecutionProvider']
           providers = [provider for provider in available_provider  if provider.name in possible_provider_list]
           print(f"Device providers : ",providers)
           for p in providers:
              print(f"providers  details  : {p.name} , {p.ready_state}")
        
           ep_to_regitsted = [provider for provider in providers if provider.ready_state in [winml.ExecutionProviderReadyState.NOT_READY,winml.ExecutionProviderReadyState.NOT_PRESENT]]
           print("ep_to_regitsted : ",ep_to_regitsted)
           # Download and install a NotPresent EP
           for provider in ep_to_regitsted:
              print(f"processing provider : {provider.name}")
              result = provider.ensure_ready_async().get()
              print("ensure_ready_async -> status:", result.status, "diag=", result.diagnostic_text)

           # Register all ready EPs ,  Add the provider to the app's dependency graph if needed
           for provider in [provider for provider in providers if provider.ready_state == winml.ExecutionProviderReadyState.READY]:
              ort.register_execution_provider_library(provider.name, provider.library_path)
              print(f"Provider is regitered : {provider.name} at path  : {provider.library_path}")
        
           reg_winml_end_time = time.time()
           reg_winml_execution_time = ((reg_winml_end_time-reg_winml_start_time))
           print("WinML Registration Execution Time: ", reg_winml_execution_time, "s")
        
        # Step1: Runtime and model initialization
        options = ort.SessionOptions()
        options.set_provider_selection_policy(ort.OrtExecutionProviderDevicePolicy.PREFER_NPU)
        
        # model path
        onnx_model_path = "mobilenet_v2.onnx"
        
        # Prefer explicit provider selection; fall back to CPU if QNN isn’t present
        providers = [("QNNExecutionProvider", {"backend_path": "QnnHtp.dll"})]
        
        # Create inference session
        sess_start_time = time.time()
        session = ort.InferenceSession(onnx_model_path, sess_options=options)
        sess_end_time = time.time()
        sess_execution_time = ((sess_end_time-sess_start_time))

        # Step2: Input/Output handling, Generate raw input
        # github repo for below artifact: https://github.com/quic/wos-ai/tree/main/Artifacts
        img_path = "https://raw.githubusercontent.com/quic/wos-ai/refs/heads/main/Artifacts/coffee_cup.jpg"
        
        raw_img = io_utils.preprocess(img_path)
        
        # Model input and output names
        outputs = session.get_outputs()[0].name
        inputs = session.get_inputs()[0].name
        
        # Step3: Model inferencing using preprocessed input.
        iteration =100
        print("Total number of iteration : ",iteration)
        start_time = time.time()
        print_first_execution_time= True
        first_exe_execution_time=0
        execution_time =0
        for i in range(iteration):
           if print_first_execution_time:
              first_exe_start_time = time.time()
              prediction = session.run([outputs], {inputs: raw_img})
              first_exe_end_time = time.time()
              first_exe_execution_time = ((first_exe_end_time-first_exe_start_time) * 1000)
        
              print_first_execution_time=False
           else:
              start_time = time.time()
              prediction = session.run([outputs], {inputs: raw_img})
              end_time = time.time()
              execution_time = execution_time +(end_time-start_time)
        execution_time = (execution_time * 1000)/iteration
        
        # Step4: Output postprocessing
        io_utils.postprocess(prediction)
        print("\n\n********************* Summary **********************\n")
        print("Total number of iteration : ",iteration)
        print("Session Creation Time: ", sess_execution_time, "s")
        print("First Execution Time: ", first_exe_execution_time, "ms")
        print("Average Execution Time: ", execution_time, "ms")
        code_end_time = time.time()
        code_execution_time = ((code_end_time-code_start_time))
        print("Code Execution Time: ", code_execution_time, "s")
        print("\n*******************************************************")
        Copy to clipboard
3. Execute the *winml\_JIT.py* python script from the working directory to run the model. This script uses Python utilities to preprocess a specified .jpg image according to the model’s requirements and sends it for inferenceing. Do the following to execute the script:

python .\winml_JIT.py
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The results from the Windows ML inference are then processed to show the identified object’s class and probability as shown in the following example.

![../../_images/WinML_JIT.png](data:image/png;base64,UklGRqyJAABXRUJQVlA4IKCJAADwugKdASqKB14CPwGAuVYrKCYpIvPpGWAgCWdu/CJjRMZx4r2/G2+dYBRY72/m9//6cf/x0/NuP3Un1h/6h6gHns+mTktnlf+3enb41+uf5H+zf539fPTf8e+k/z394/Ir47Prv548gHnX9Z5n/yz72f0P8B+/3rb/tP8T4s/m/8L/0P75/mPcI/Of6H+vX51/IN8D/zP9n3Jmmf77/nf5P2BfX36x/2v8b6JPw3/O/ynqN+lf5L/1e4B/Ov8X6Uf8DwMPyP/Y9gb+bf539nvdV/vf/x/qvQZ+f/7n9wvgN/o3+N//H3/+Fz0vxVJRAOghVEA6CFUQDoIVRAOghVEA6CFUQDoIVRAOghVEA6CFUQDoIVRAOghVEA6AhZsIDhhqMFhfE077ukSXPb7OPeq0LxMBvUXTdsSOp6ZhSqyv17r1iGLj5KrypipUfjMUye75sDftgfd8c0fSZxPNzvDBes8neX/8J5HKo79OIO7Xv4G/OguJPuQBBaa1zyCrHheyBiFUQDoIVRAOghVEA6CFUQDoIVRAOghVEA6CFUQDoIVRAOgheIeknLjhF39mtdW42gKvMjoIrzFVIG4X/1b8JpB5KY21GPli0D196jwqVq2su1wPNMMAnoRpceFNNbaFL7JAEcplSJ3C6mJxW30iYK6xZCmfNZkFS6Njm/2IDuAVd5s4DAaQGoDQd78x9xcM+BnFwz4GcXDPgZxcM+BnFwz4GcXDPgZxcM+BnFwz4GcvVzAPuoJAjOxEMDxYiV1yGc4RSOpUI9K9hu2m0jgXjbhkzdfNPKq0aO9Xu94XVyKB25fbruxUXfoVbSC509J75h1SQO0ejfLtsRMD9KdRyrYooUxoaXpB4BMAbjrSqY21FKpjbUUqmNtRSqY21FKpjbUUqmNtRSqY21FKpjbUUqmN8KnKtv2w8ZcsmHWNswBeJmT71bVaF78XlC92s5VHOsti9Mq9HkZ8H/SWdAE8sVxiRoirFTO2DKZ8rn8kPFk07Pl5KU42A4aJtXMonaFroLd8XDPgZxcM+BnFwz4GcXDPgZxcM+BnFwz4GcXDPgZxcM+BnFwz4R4mZ0qVmwUJaa6BDH8JwntjAhmn0nHYqKWEWoYu2BBI+bYwUcZ2O6gVSXThfqzO32+fbnyYfvfp6NQNqJp/tKXwgcKjUw2dNQeU12hxS/duVA18H6qHHFzG9Qx8nW1FKpjbUUqmNtRSqY21FKpjbUUqmNtRSqY21FKpjbUUqmNtRLolWTb2GFaSd1iE9nGCwvTGKJ3L1ku5luSY5pO0h2jS/t/BOrcpvz0L+VzT7mwG3jX/kv8f5Z41fZXju6zAPL5igdbjopUttD9wfErObb33pzP5gGecEyfLCt3wOVV6qY21FKpjbUUqmNtRSqY21FKpjbUUqmNtRSqY21FKpjbUUqmN4Nnst+P0LzOUOyiZU5q4KT1Zoq6Sv/+tL3HHjoAaY7Nd1Z1+DXqKZeMo1GZT7xolujz2UKv1MyKfC/cclv0lE7CHzUTpJArmTxnih3ng9LCnwD1wHwljP6zm6jT98IhL73B2IzDxAGSDwO6y6o2hJgKIxCMVldxvhgEmgTzQykiCv1UEBMLxNdBCqIB0EKogHQQqiAdBCqIB0EKogHQQqiAdBCqIB0EKogHQQvO2MBA/Ji31Vh6W2IDV92ZUyVjRiWV6ImxqWhSVGFwgDauhPjNUTMeWGfD+j5klEA6CFUQDoIVRAOghVEA6CFUQDoIVRAOghVEA6CFUQDoIVRAOghVuNqNIVzvF0nB3NnPZehyx6RgoXxHwZrRDlvtAlgjIf2KAy76McbJErAMw4QN710fKMMx5oc6e/WukCAnumvUIpRo8VbrVb8uNSbMfmotGBKJWKtXh49gJ88gH22LR1XsoeayolNTtHDT3ekmRJr5P/t5wpjl50Krjdv1k0H/b180esvGyhOHYIKc5jk+4uGfAzi4Z8DOLhnwM4uGfAzi4Z8DOLhnwM4uGfUS2J1HsIi0biiXYE0HCDXfn0FsbHcM7kDFK4PfliDsEPkCAIOpX0kgcbcLpI6hdRSYglS5xPtX0SwaSCc+f27X3Ey06duLBchKSt3C1sZxS4+jTcPG6gdQrodNZmg2D36apdxMgcfKuXqlKb5AvhEL2YoqBjG/KRIV7/IUsZsXWRhMrMyvv7ksQKDrqTS2e1uci9RevALKwNMgmd9sTwg9idmLQQ7xXK0qmNtRSqY21FKpjbUUqmNtRSqY21FKpjbUUqmNtWIEKzyGrNtRStHo2vHNWSNfE2WmIYKIIISayDl0nnBnEG18nugOuaLp64uSJdmMuWZPsOCLZu2MegEOWvk8XacTvuOXKkLDQEIC+aU0uU0r2JJFQUcmOFMU5x+RioE0yZt1lCmLYh8pCV421FKpjbUUqmNtRSqY21FKpjbUUqmNtRSqY21FKpuNHL4I+P0I/nugapK5NM4RpkecTHBu/3eMgzqqBoSdTTq0zz/5fEZqf530dmRA54hgDQ5iHYQLkBWPyQf8kbc3B1BVluP/b9gajOuqRQBZzP43JPp4besR0LWHhJON0YdZiP1gD1bzDkxGtuFBQpCeAzDwh81p67dTeZftOQWjSUa6zUEWwEJO3aHKR6tLoU8nY9RSqY21FKpjbUUqmNtRSqY21FKpjbUUqmNtRSqY21FKpjblaxH2wKq/vtgBtwrlGXZ56KO7S46tlCFjjrltDmBYbRJ5Ubpf1gigqOwHwZxZ13CDtjYjJFHrgkwj6Eg9rgis7rncnKPUwwXMjDtpvG+Tfp1mWvbblIyTg+15G4dBBIvi8+eye1vUcWz22pk5KL3nzJYCyroYBKmpcA3ELVgX+4Yzc7C6QhMKjSNZ89+NCUQDoIVRAOghVEA6CFUQDoIVRAOghVEA6CFUQDoIVRAOghVIKgnuJ9wQuKSnmRZ6+kM8PUI5v1DGIC5Vgz7aBOasIA/juMc3uggcdltqB+I3LKNU8ycVcDicU+QYZYJ2O3U2zitDzCN1quJHi6J8RWtBwknhxs1stHqONlhyFFCr3a2qqnrpINRDpuNjFy2MRXWlUxtqKVTG2opVMbailUxtqKVTG2opVMbailUxtqKVTG2o09K72Otxo+UTr/858VNAT3yYfljL1FIV4xolAKTdRGV1mSxyQwn19KogHQQqiAdBCqIB0EKogHQQqiAdBCqIB0EKogHQQqiAdBCr4HZkVDx1/o4icsp+B0fOGfAwlqIiKFSXCxpE7nouy1Ze7xw7RqUllGAmVTWRFHwPBRdbm/mqlk1RMcMJGhi0M3cxsziWcIUyuwdSCLYwJkUlTu5/vE6dtAYC7BRxdSTImrDQ/pBftGdG1FKpjbUUqmNtRSqY21FKpjbUUqmNtRSqY21FKpjbUUqmNtRp5GwASl4hugqiRoVMocKXJTk1nZ+opVGOk0yd0xXE04pa9LcDTIiptsyVubi5W6igzPTMEZNNiV9MM4uGfAzi4Z8DOLhnwM4uGfAzi4Z8DOLhnwM4uGfAzi4Z8DOLh6plOswnDj/mL2g1TjitR0n6y3Z6sqvGsQU/kjgS9RpoCgNbrSqMdKG27318vu53/HUBvVmnZz0kyHzag22v0qWiCLfj3bLZjx9Al5AJkfcNnqPptfHzVl83zagYfYtaO4oWIuPG2opVMbailUxtqKVTG2opVMbailUxtqKVTG2opVMbailUxtqKVTG3K1iPuUblyjl+eimbUa0B0XHrNpGGJ2AOGQInxTmausZ/OugqarEqs+6Dd3appaP0pp1Q5BD7HJD9BTxiqMqhi5eT3LbE6xh8x4LW+ajZ/Hp5aDpwrAOWxIWvw12yDOGyIURnmSdKhfY8B0SxqZWLU+sG8oAEa+gE34csF6MYDQMVzdWFK2nwGSGjQbtiTvyK0K2UVctR5sT5h2gexbeh09pSMe6xOxHOPqWVKE1nMTvEaWWUfzQM3tAJ8n9kkwvnoVp7KkBy7CXDDoOXjbUUqmNtRSqY21FKpjbUUqmNtRSqY21FKpjbUUqm44QWSxXMVxTKgkXNVULdeTQWQBsor1xkqN09SX7Gqi94ZWYDNKoAe8gz1aKROL8nVm+Lm64lN03DuuGZk6fIuKm/Go/GiXwkkwBEJNvSL3rNgp9xaWN07D8MlzQDdU8ti/MYCDNzsW8m9StOdp0kcf7pjfZjvgyZaCMXKStc0opbuEJ/Q46+sFU+aOo04oJO7BL4iGLgDSN+CNjIroIVRAOghVEA6CFUQDoIVRAOghVEA6CFUQDoIVRAOgj7dtRmQRxIRpBIT3KdZXRb08T1lgQ290CeYStBcMzgtfAj57mvm1gUUcmFAov2uLv5UDGjSop62isLzAweDu1PS4GP1CiAmO5kDu8CS/yhJUhuXWWHNRq4wtK5hvxIB6xZ6lwIYy8PXyfQMiYxF1w3tlOvj2NX3eDhnFwz4GcXDPgZxcM+BnFwz4GcXDPgZxcM+BnFwz4GcXLZutKqZ/0vlfn4GEtRGr7EjZPXuobgI9o2S6f0MUVegEThGQBAodL8KlYU7EcJQX+6GjGDSoqTrwPDVDHRFAobZVfANfhmTzprdXhTwNFHtrBm4TR69tDtKq6joYBEjlRCll1LQT+hNNMqzImSmLLLsRGpFqdudMsWQD00bttHUCA8TboPtg5E88G2qIIk4YMMSnFTPPIksjQDOSNAHQQqiAdBCqIB0EKogHQQqiAdBCqIB0EKogHQQqiAdQRdD7IjtDZEd5r6cAqjruOv7IXIsoQujMb1rNtPnPIpPELEx5H2kiSdBzXEpH9Tz+w7L0qwuP6mS/AcP72Kn/emXVbb5A9c2ihLHS0QYgRDR/KhXKPPd/VAMSHggIDiuh1ORXrnszdzcfZOzgxDh4xK4WwVUivW+goH7hW8vXPoagTK7eUNgD7pj5dTaPMN4agY/uJzvmBSN31bYM13KIRMzWl+vUCmaGTqw7ETgSsZge9QHIgKBSomLSqY21FKpjbUUqmNtRSqY21FKpjbUUqmNtRSqY21FKr4Uw64T5Cj5sR2TAPPW5KzpHB6SpOBbDsJlFi2tTBCph9u9RpnlQwm+kYW17o/3JZxgwv7HdHBeVoskxOFSMtH34lKIj9zUmkLfIDal68D8SlwhRDHis5a1LLIgtAwQWOVEpnKMtax87L+3hEZNPrjsncYBpi05BVqIUNzWqIJ/qUebWAddjCqqeP7FuehAYF7vUC8l2gjiSAlEA6CFUQDoIVRAOghVEA6CFUQDoIVRAOghVEA6CFUgqDMWn2OdND6rGIJkzMwtvpe94mXvIPI+1S3OOpw8Ru79yZMlCH7aCLMXvfhNWBTzAteqdNFL2XQS+J/HLH8AOI/ukrcaRzCPIgdoyEmlSoeTKdSU9cS590WGzEvjGqGYnBRv3ElXon9AgUBt0NtFS4M4WCH3AaUboYzgsH31NGhHmr8JWkRZvLsPYE1KLR+K7VKjM8G4Jh/JpTdNGGOi53kudySUO7YRyUB2Ipfk9g/VAItPxQnx72/muRjIi+1M8m44C6HZtqKVTG2opVMbailUxtqKVTG2opVMbailUxtqKVTG+AqvxyjdNwwn/Ikq+IqTBGd85leLIlC4roYeP4K3b78ad3AQpgaT1Ea6CFUQDoIVRAOghVEA6CFUQDoIVRAOghVEA6CFUQD+4hsyf24YggJZxW0I2x2KTpnST3Qqu8WWFUQDd1k8aBHNBCqIB0EKogHQQqiAdBCqIB0EKogHQQqiAdBCqIB0EKogHQQqiAdBCqIB0EKpCLZaMdNj8njel8yWU7roZ73oP8sNmd6D5ndZDRasSg1Du24ZTSONBwUonPM3DE7bXak9sHjMHhnBiDYdq/DlmdNvVePyyIG0EyVyT0X71CiD/U1gyRMio/Kbw41Nx/HJnxwvrve71cCxLfiEcQNzSo9nYFEA6CFUQDoIVRAOghVEA6CFUQDoIVRAOghVEA6CFXpNtcN5MbailUxs95GEsE4T8R2G7POI7Ddni9wtF7AJvuCCm2ZcGB2HSxFHQ+AtVQd772qoO997VUHe/MfcXDPgZxcM+BnFwz4GcXDPgZxcM+BnFwz4GcXDPhUihnwM4uGfAzi4e9h9XpEl8yS7kTuLpr4mnfd0iS+SDrSqY21FKpjbUUqmNtRSqY21FKpjbUUqmNtRSqY25Ny8bailUxtqKU7Q5Ls9kQcEi0O5FhqxrkgFqXCxkCq+0yI1KA5snYDz+mrZ0/BazG5390sDyz62+29FwfeTdJCWRqDNondn+F4F2KaSoioMXDPgZxcM+BnFwz4GcXDPgZxcM+BnFwz4GcXDPgZxcM+BnFwz4GcXLE2opVMbaeW2amdG8lpUn/S4xuc66umovT+3eWLpjXOdQVWrYAQ8OPsIVoyv7wEjbakAkBjpGZ9z/qhiF4J2uye+aUoRr/GwT17SgPwRNo3WpKreppEhf6S7JVaD9vGVhg+/kUaBr7vBoncIU5ZTrTsrb84/gWoq5mRsWeBdBnFwz4GcXDPgZxcM+BnFwz4GcXDPgZxcM+BnFwz4GcXDPgZxcM+BnFwz4A7SqJUMfFFO4QzZrndPHS+EWJwVVth79lMkwdZQECh/4DgHBpWgoWEWLN4hjirHhhOF7dPpZcg2V5FK//hQp3lCw6qckMlCj+hOaWmT2BIPp7vWRYrwYfL1vEX3Ao4Y0C0DytOXAX8/g6BayC3QojzogT7JRAOghVEA6CFUQDoIVRAOghVEA6CFUQDoIVRAOghVEA6CFUQDoIVRAOggI/pk7pPfGtGxd8BNXdUNhvUivcAcZuQctQgz/IC18qE9YDH8UZe5Yxs0fvL9aAHxau9bteMYspCkqm4XLYuPHeVMmgsk3IGY75IKMPzYc0TqHK7CSZUU9ZI75PWOuHJYkAryNSOul8opnI+vYc9WzeD/vN/WQlaN7UClSr67v8+qab40t+E+uOon1TrwH/17seXnq+eXEFHCtHajWseuqB2hTQM4uGfAzi4Z8DOLhnwM4uGfAzi4Z8DOLhnwM4uGfAzi4Z8DOLhnwM4uGfD+Uh8gSjCeNYwZRBdUYZL8daYTAzi4Z8DOLhnwM4uGfAzi4Z8DOLhnwM4uGfAzi4Z8DOLhnwM4uGfAzi4Z8AhPiN4O0e0uHt7NgCRSpmgV0Tacng7dgCMgJFi828Nmbt//BdMSniimomK8VzwoMwMtaT7+p6Mxi+oSrC6E12uHEpMwGOeTnUxPsZb6nTBXwPHmeaDlYsKqxyx90zAhNbcogHQQqiAdBCqIB0EKogHQQqiAdBCqIB0EKogHQQqiAdBCqIB0EKoT2/W/abd3wVe/LXgXn3IBDqPuprfMHoTx8N9NAQz5gyRF6DifDYr7sOkRB7wg1sHBc6kUApGB3q9MnQfLw0O8S0alxDqA7qDIMeWMb8CH4XaiawHSU9a4lUxtqKVTG2opVMbailUxtqKVTG2opVMbailUxtqKVTG2opVMbaijwAD+/y4swwAAAABR3SmRcGm6T9p5oBI6nMIsfXlEDhIjFZ5GAJUKBqxmef5qCHk8JkOSPIYrHIrxyycVDs+m0d7i6+oGo7827U2Z6UyzciWyqJQO4Mkau7hQlVoN9f50dEit4uS24joaB25eU0amgD67Pc25BYjthhViBEu1So+0ZZ3TsU60c1Z4XCJvA7TZJac+lfPK1x8gT9+1vLNQq/qu644Pxz8/V8XUQ7qT5KFrVX6gfRYRryn7vGEur9TyyqnuL5D9WvdSLqtc9OUEc8INRhF4dZeb9NCoGo+HLpjLUDOxD8DfH+3xUdlsM31Je/85mnrfEnDc7CrfRwmQkK9ngpR7B8PQlHs6JrIzOMPszgWqvL8gCaVt0kZX7tkPmoxWosHaX0blGsajJnal7x87bdODhQIwN59ml1Kac0SwTfcO7Fo9RU7Vn/zDXMPOGzgbdvFMNQBKHmJ1bTpbKrMuGE60ooEeQ4Sb/c1rolxXRR3djHf4Nf0eJUq2QMZTujbh9cnNBBOGdhyUu456vQFQc4Gr1Ly0ymqRHC0hzDV3oN5EZ1ynGufrinz1Tt4uOLV8g6+O8Kp1CRa+utzr35mja64hg+v0oXw7/CxbLpCH5T9xXDr5DgPBIunI0eq4uGndBeAZvj3wiqcVpDunq4bjgQ2uopFjlGApJIFQKUUbdtdcGSq12UiFxIYeKfKMzH2wHXiiX/80TjISZnRqGrY0ovCRI2Vw4cbK1DENkrLXjH1BtMFMjHNmKDNermvpcjA/ONESLBkuQ2uKPocT12VIV61BJUnQ9W7KenbGSReIMZ/1u9XRNmlutBMwUdmEhAPQ64iWHeqvkx85BWEX26c8uNWu2YadHJEYtGHyFgGJsEWgJtWz2rXq7zyXZ53dFwZTFil1Dhtn9ASHoAgfykAXU4f+BLWHM6BdRZeSDxcWTprqQJQ8Fn7Exo9KZMxa5dswiTL+nbgn2RnSLWDKFhVf6rkjT1Cw03t7ph1gojI0sdLF0Cp/wqZ7GVhp9AAAA4SbzhuXNFQHTKv3rlwlCraHCzyQA6p5JSCTfGZ73eg4U/KrXOc2sCETid0yP9emu4wm7D5swM6c/83yPZnkyQv5ARSISUZAZcIIuLgPg2nxyMIS6EausIDg6hQawFU5auw8EViyvU1ECdhQ4jc14rZbkh1D+SHg4ldWSIozcfDSY/QQK3TnulidluGnb6HtcHBPXlMhFmICNwqBy9pUePXQ9wpYo12hFihD/P2TYuwpq/0GvzBwnzDiOBtt+5kna0j8dWonkcIlGfgIZQVwWCO9CyhzEBEcGU5vqx28VbY8EkbnP0GP6k5G4h9IU1915NP3MWT0+rjdCXUBaLtuY2Zk7TPWUMasm5G4vgjp+oRJ8hDCktrDbVldpgF/qDUUacbqJ7/MNRzYv38ZQx/oW70hZweISCI2due9NWOVhVQTiBN0CnjxpxbiuqYNfUNhJWxW2RXGDJTaWJfOIhay1wq1d5QaMDcewk1zRwDERWj88RHZEHpDGYp2stHJANJB/EExChPaP9g1lXltyDhLFRBz75wI0ghCPpR6B5kht2hCWcUOoM+nqEFJ8t4Qf44G/PfkAJYMlGpTyEvUiqNXcDRQ/Anbor7erXrd4g1RYRzuD1FFKNFhy+n1zmUWOyqTVOlQZrHyUej5IFQJFIYmm4EaaxZffHjFJhX2PtXmAgelrN4TRte9Al0CMXODE9fZ+8AkkWcflKRzZNzmobCRtlCkhrtHiL3NqisMojiPbxKxog4AAmeII7XkPT2JNCb89rJJyelD5Q/SQAAAAAE3RdM4+qv0wkT0mcYpkJrGwSLKgNNcvJWr7Zf3BZhoRC9pIubIAzRYjHeO6Dhv2XdqDb85WwEmBR112qZExlkqbjVxBMNeb7CQqbEDi26LonPkzO4pA0yu0JOszHIunKYoFh2Aw+Lailxg3AMZFgFIFzNv4tMwDOCdNYhcSvTsMS+lg0C7moF2LBXaLg5UhsN9fNkKwPxjgVrheLabgmCuSk9arALkAOOTNSwgrE2+4iljNiDqrdnOj7LshLWwZK6ej8GTAFXuYISqH5EDloqT8mzO8nhWZqOwT3gSUrFM59I8DGZipM7Zo2TaVB0ROZLHBybPXDmrG2qp75fBI2aQnvPMY9Uo3+mrOYI4oMr241/K8h35NKvcqcQee3pjfRwXcP6bW0EEiDaXCfk86mTssL8JJO5zIDbWFhQXAbd4XZJ8k8Gj/6bhuonuku3ZRwuI4OKtLEkOou2LY1am4Bxirkrgopl8eYSUMgOw0gG2VIgxbyL9yDJYba/AeQdTRc1p1tbn/WXY6++rxzbVBa3PHkTE+3uDaIRuWy6IwhBnQ3BoShzkDRITPPlW0OJAbzjaBzvJev2TGq2+iZnuNi8Hce4C8dfq+Msx89XgD7jflJW6fZPuyU/jTfiqiXc5LzmBA1H2e1bDa4gAAAANiiOEyRIVSr//iVk/uXT4FGWKz9HqtGl+3fVt0h8/P4JSF62M92fc9QZbH36/9h1T7QPNe8Zw+IuGkEon1x5xWOfGeSv070OIuFK6LISIMvYzP7rXhg3tctLIpqmtISUYC2+W2EjAnbZeJF5XMFZeklpn0k7SMJ3xlwV0ogPfeuAkiNF6ika0rmbNgJR5/Ad2QVwl8WbgEdwbAbSY0VKIgFfJDeVsVjm/tMAi6s6MrypfIpAum+9Pko3PmIwFb1KC4FtPIm5sf5bEHwIFM5wIlcYqK1Mf7RMjhdck/XZfTca5pIIKZfkRV9huDI6mTsurenJKd4jpBPxSsv3J5ZqZtDvGna6JbfihLKXGWl5fyo5q22QINyKnkn+KCPG79jiVQJO0IQ2KWLlWVVpFqzaag99hEkbEsasTnZ2HLS6HhmBEZWj6Zj08+JWLrGgJ2YBvkJL7Tqsz3PeWxlwn43AG7bVEpq2srC7EX8ymaRjFfevTtmtbPMkTDUSSnQbnjXLJJP5I2Ah4gTS2NaqeaAXEv+KhxenBtZZ3fkY9GoBNwhP46hlwN/qPvT9nUYfJ8LPYU9/Ds5G/azzj6kO+fIm2Lm6LFrUtmpcS7KxIGrTISbb7W21VtYukvWnzGrG4luxcozRI55/lybxBvtyXp3gV1gTLv3woXPfYXzOfGmNBXTrU2VSg9o5aSCo4M8z2SlM4nqXSkYQD3xErUws8L8TYibxmTwLIPDwSu8j192UFXFZ6a/eEeVAq2YAAAAWNBvTuHnrdjK5COP9fPL11QTaBFRmV4ov9ynjiTz4BGzMnI1OnTCWw/QDeqfslCOXn9l1oEkIthZSkAtKd7voBy09olOmT6N6ze0sq1lvIRD380g+uaRsuSDCMDjSREOvPJasvIXjafrOeL8gdtaUpI1IiY5ifWfr7gCRwVKx9uezg57syGtngjl0qHx/HCGAtiC3JBZvJqGnT+dcozhkjSgAXOlYXghJsckfZCBYjn87zrN8iNhQJJ/uU/aqiZvkp5kyqYXoA41WgaAtAHE5Uu7WY56foO8+2ujsYkTd6ARqRoLzl1R9I/LCT5uuRFxuhcUngXgd+0xvbAc9W9mSQ8w/Q/Q/gnedxvQJuAMjjJR7Tvq/m01bIpDP1CVrv+rWgHCNaNTbZX1/t+HBV8x/HJhDUOzbAyncqCfGEHUiinWv/pkV6qJBIQ5Ir7f+CTEjyOmHrYAb70sHnaD9H2M8U/1ilmPTbRivi1RGE3Bumcjxcj65kEQXG7FYQ1oUnM3Su9daqlmZtEFRfL3oiIbqoFGv4EgvsBQYZeQ9EPJ6T3v/SegnKdMore7V4E7LwiGDEYBjvL5b70bvLQpJrE4zifdH9g/FZSNYgjoipEy4OcIAsK8qYddbCkTAPjP7naHqWGCwciDryeAt/P72DT1KSiNpIdYT/qZgzHxMd+97k3aFHF74ieLZc4sZM6iUgdhVZEqGe0ipEJ7SEI3ldxIGhpXbR6VmyQKNqsRUaWUXp9Wa6SkiXUP/VE6JKGpnlbzK+A2TdQiuxY0Mmr4OHx9jS2sHOLvCU7cirOP1Ojf0shxTvnNKvYQbEQ4RQPShdjtnlVnnuRz3/9VcqJ5Z8vbUE6W6pN274R9o/MCO55u9KmIdjgDBhCWnJxFPxc1hqLzeIOjAAAACWFAOcQqYZASLaIAxkFNgzzrOfOsUbdspJchUBwAZub38YHk1cHKOQPWegb2tQKU6beKplKfOEauYqPPgn4pP02mW0Y7/w7NpbVbbCuFCr57z05pLgfXml7YtlzfF23X0ZAaoMGTpXSfYbM8K5iwps91uzJAUd+5xs8FYfaubyDstIPriZzFKHZrnkQzmbOgrB9trvqbNOHkxpISYwQj+SYc/cylneA2DynP1GjuvRarkD8Vh7SevPRBg/KMe3zzYmqTeRPwimXyUVm8hf78VmABWrOHy3rKANvJUxeTZ9D6orSJyQykkuu8ijjw+PL1o0aBO9gRPxwY/Q3nx+PSB8Tz7wQ9FMclmF+fEhE4TNruc9mWqAx3AeyvQJfJMBOV6ClTCCA8Rqm99F3Y8FO4u8t+8KrOGh4xfoigBUiUXj+3a+p0eUp0Z0+vtoMMHBsUqmJ3OYyUoAplfXamd3nC1JIPzDtUZUhtTSgvdxj5y2qIXTOpK5d4QapBI4URokKjXH8XvjVZaO6OkJxrp41Wnl8rQCiZmPDohToJieV2Uq1pAz1qOaKqKUAAAF9dms186waCtnEyHVIL6SCZmnsN3UuBJ5k1vfKF0OEPS4XoMO4UhQKOe4ptGce3XFI30dOmGie5qAQ7zC10ybP2IzIR8fw2ix+qMLJee/qbebtB6607D9t5fuG/PGqHZ9ZGTE78MiqdVaksTUItx5+/6LbDhDmVJfCeHe1QD4QD+BV6akkNt0QGe2s5kOkqyabS/fIPkrYAKnzXtQa8LUwghqeVHpYCejDG1yzHfeaLfdwkCo2wz9/2bd0i+mugc6Tb+RLr6xSJVHRfRvKqtHKS1FCnbxQ087rMP56uoP3Ml0voCnsVJGDi7qX7uORLMYII5d8qXw8PwRXjem39BXLyWuLCUYHRE+K6nQikDOgpxeqHYmts0UZpPj9eTjGOhMCGgNm2nH3lP1ElAV+ilGPZ/tnaGGUom0X9/hyEeVWr/PAULizda0lcsnmjtA8/f6fy/EkwfF69WZI3bn0V18eIdNPeMXHVCaaJgHwTBvmUmRA3zZyVX+rZWUn9bactWIo0RSOnm5lV+zkzO7XcLSDaMWVBlNEv9pG+EKFX6JtNtuhPAU5OpZpI1dq6HUaENpur6esmQCLcjCKmgNAwlhaTB84B08fa7GGb7To5bo2VDYmiNYCU7XGSs4+xUDr0JSIxMYcGsWrI6Zr7BwmcjqVtqzasWD2OOPFTMNdDRYuRfbii1Sjt03zYOSuC+YUJJwjKm7OL0QwQiL4PKGqnBiQNNOLcCV7XB1lZT/7mGxgtWSLxX/EyxuAERxItUU0p8R97rjeMY/9vW9/+ECBtd/BsjIPgJ3b7Qxkflia4gG5WUVvkpgzzL8UHU/9EEwR/xbglinz4i9538fC1fsWx3CgcRtdtgeJpYruzyUXJh6Mw+YpvO5pFuE5O6ZIbgMU9y1nuSPZJNfznm1SAvtkWvnQek6ImxQQtLP9xofKNIhSfzGqusIKfIaBvd/A2n6Lo8JDqJ8aAj/lgEZ7OKdnYndKCqEkaSnZ8czcdcKHgIuk7FiuVvGiixKBSZzVTCYIGbTZVWzEtIHHURdbrtweFc6FTguSgpopg0SusDSzholrhzi6OtZ8ixYSbH169k7rDOU8sFf2Pzt5KmJSNtuKo25lOzT7U5271fhrYQq2CJ5gQihqXrR2xMJtCkgYg01emXSyqmjqTdGU7GIiXB3ImToubhzohGgl2YFnbn8d1A74iXF8Nor0iM8C3VxC53LfqRe+FwhybNket9b8JtBoL5B3eAVCgZrpwkuX0MUtoub/H6proXSv6dhZOg8PajljdE/wcsRcR5MtbeZVc68lg088sdoeVtCRfMLQX0JcHfx4V+wYS1lh4UAAAAAXYESPllcJVQ9V/4JnFK/v4OCCGChfn0y5bXDUyeKRxEstD7lt3StF13b6wPGWWO0sMU41EZOwbbxUZ+mlHGUQiGXXPdRDedtDBh2UkzHs2FMnSzRvnMoUE0nzCFQoOiMG9Zvmfw//2KMXQNjHriUTHDUQV4Wrie/9uvXXOlV/L1cWLczGTtNY4QfJI7HYy16OJ8pxWCq+DYq9Kw8yqAZqA31BYAk1uUaIJivRcsIbvj/1dqTzWs+yJOFKPxcjLpxLkZT9qtmFBSDToPiDPeKrBkOch1RrI9MRzm6bKxAgTYXY6wYnv7fVUoQm9+00Fpqaho25i3DjYD4o5zo0O/eSV+CIIpAAAAAD2p9Xvm1SND672F57P+R1Ob8JsimwxTX7G7SX2kgc3zY2lboYXbbdSPj4+VcL0rAFuhukjdrC6cnmIK2TtniI/SG0WNGewZZTPJDXUWOWS3JHTDwPNN4WfXhqMI8NKavD9xvZozDGDelGVrRNXhhhU83S+lGsTGdS0B5HerkJ8trL64vsYFfCIW+cBUzqQJEiN8DmP6C2Jfvz7onvGsQoWNjcQ0qE0efqoA0/w32Qv1DoyjFsOXiHG0yYdpXyyaBWktqOtj76aXxQuCmjUu8NcFAeFE1ucFShzZEsSA8EFl08n+gEHCtENL0ILJ2hinV7fcj8KRJZlEHh3GuU7v4BtF68ct8uXXEF5dOoVVlGJJV6EUnt9893ySw9rwlhE8n2pr4M1WEbnzkfM4arlnNZPelD8CWkrNKtQIhq3DbepGrskGrk+zAKwHrfgoFTjFL/JEj8qRmeNxQUbvcY0orKU/zc7j9dqEx084Yt2rjmmmcmVOsnTtcd+cJfkJYYpw9x9xdmT3Pj1wwv5sxXkJa4yHAwQEWQqnhFlKgFLp3wNXCaNx8HI7WOVgj+cJ+zK9zdv5k0bWJ4xXc8U5Ra+aUEIioho4LuflsQqThiMSnPDwD21NwqudxpBffF62REFZXcYmDtjHlmKVaROcJVIdd3nWFnq1Y4fP/8n5rwXXdyAgg9cse6EHt3QSHTnpSXBVNRobQJ0wSuSRT4+Oyi8BLYLD2GtoMdEzQcE6crTTm/Gi/cz4fVKvwFXBM4c5DIJL8IZY+XnV14jly8P15+LMgtSB6bOy9bR1CaCtSFk1GOXCByjISS++mdrrI48BPct18aZWWjTY/eIdaRdmCyulGUyksQEYa9NaYBzmYYGvAS+LMyc4Juh6FxvP+ubqCtsDCSCSGvv8Z2rFVYC3Pm8HPvXUhNeEpvFNXA5KntmuPDGm5/oxIfu+9FQbBm52xrFE8KlLV7wbWvXhmLT1/aYQxc0co2shPgGdZq0xAf6td5AKmygVTbMP+ysOzOO9+NT8akSv6HwkbNcyBgEUj+YFuF2vm0uPPcSsduX79iY0spQ6b8rnp21BxebMCtQ3i13UKgTx2UwUNDD7XSWv+yGdEcqw4o4tZ+IU7Itq5u0AbX+HWQBZq5WGk7MlFRIUZBj/tNsMHG3RmWj8hhczce3S7cCE57b+WDr5HnA+N0hjvUb5IC/LmOt/zRn6HeJk2wMsgqlexW9YY96hW3Ix+QGbOe2Wh6+g8spPzm4k6gcV2LXzOjF6Lbc4Pi0AKjQronKsBDo57jzMSos3HUSzG+0tiF/MATPHFL4jrN2EkK9ul6quM95aGrH5vZx4ZmnxceXD0VG6bUxc52+F9aHQFf6UcYWHLelyUkYkvRs8GuPjv3AJ/JzMnqkfO6VnF6OR3r/SeY3k7w/OEqAuEExKzmfsp+bPBUXthgS5tTWWhXHU7wJKAD/lQgtK2fwQwaW42Oqw6Dz/bD6G44WN3LqHsF5Svg5bkwJbyUjk124UKa7tQCZxnoFt0ab650yU46NQxWpVi+I+ureG+BU2WwyCoBwDtJl/uVsi1PMjb4d35lfLGQ9eMpn4XvhbHNAPiI0xAoArrwp/KuP3patapKib3LOvyj90uPKbqfIlcoZXR1JO3gXs5X1eDnU9TZa+TB6bjkoO9ClnST3FqZ4SJ5rTzQHPOR/GcAAAArqkRZZGNVmsJkRkm5EsV/B2pqsbMND0jrb1ombpgNKDAi36ivVRIGn9r4KDIKLau/VOD78mKm+6U4HB8pEg2wOX3vj9Mef3dSfUT7J7hvKEaF/w9Q44ROwFOVieQJv3SfK67AD1s6yBcdfpRmHeK95lo0dzRXvj2lCjmomCnsQGei6IqWd4qYsR2TrJ1xqoSWIkrbR0avWcNiwgrnonj4zzW686xgkTWGxlnSpHAQXCX+6y8cgbrOGIbmbqIp/p8PsTCbDuC5xRiBWof3605uA9R86vjsziST0Clb2IndGi4J3pEfb5q6IaVNZBfsQdNVa4dcTk9Bd2VVzQXClwan6I4Nmlt+xNehzjprj0DiyXFhEX3FRGAYQngTbr0XqWxeXYn6gzPao1kionrRnCEg+COWe4Y2mJ3m0RD+K8E5Ha2Dnv/wtzyL4NcB9wYH2C3FZxkUuzw+eyrIfLv1UMhkaSFGi6iKDy3IbhxDSAGn8HIPzvi6fmt0c5/fz73LjTgTarW/ZkIx42U/sINuLZI6oqN/9aCK+aIsHAL/dlfRWSPSYayacPN51Tjjy5tav6SYTr5TMM4hdqriOTVhHqU3lFCrLAgyk7utFlVK8nL/JltwTsTdTMXWD7XokoE84+g0lrfUBdZbrH1z3Namoqc8+QxOJ6gQ2rS2H8TqgM/Xz0wxLAoXxmJVD64ZiI0R5St1D5iA42sxkWhUulepipYUxutFoESAxXPVZo1t5VQrr2MOlRknW3NWcj+ldVqbiAWeyfaqg8cvjI+QcaiOq1ylp2i/w5jn79Qfs95CCrQDFnm5lkGuSiJcbvKrTeSnmuqihqtiBnWyv+VozwsFo8caFyVpzPj7YI4M1vJHzTZkki8qr+sZ8FvOtRzyRP0BobpqZHkJcJxCEKxmSJ0zRmdeeyVM189m6sdkFzT3vZFVyDmMpnIKkVDh0HgwQ2NqYyq6ZmQJG9cpxNXZ5f8V/gZCvtTWGZsthCrTBHvjI99x6sTfl/0YqFcaLgrUeEmIY7R9kKUGSr/XVCJjx5hgMlZsMflO1N0S1S80qOqVhY5SxUEt0k7IX6nDpHxieoOtn/8+PCXTDsh6QY7D4A55UF1PhlQQWILr8J0o3HN/uuOXU+NqgjMhtZ3XhAIe4dNnjZKVjb60r6FTQocsT+3KmVYEdIv0oJeKFDbhpPPDYh9nWkMUQNNwpybAk6v4upWRfirg0cZSn0aJa4ts/eGxdwHsQt1ubK06HA8jwjrsDSIJoDRONEnr17p63C7mF9i4PdC+1/oQqSWmWYCHUw0vsRxknwAAAXs5G2CmIMAcZDcEi3tTDCmTRDZoYPkBt5G3fyqwBWFewyKgQhUszV0y/niJ4eS3oVNqNLfPeUSgD0AiZpfhzPiCTvSiogJptyVZD9F8ftrSa2B0IX6nFTkiUcTYNFLwRqLkP52Nix6/VnuEymNEovvQXcYcIjXEpdM6Xi9NiSNU2IBE7xjU2HuTNv/sg9/QOxs89KdOsu67IC8YiNWe1x+g7BsOxe2703nGZQy2Gezplie9RMKVEb0Zck/Jtn/9RNVvwZrCz/YGylmavbcI2Gnc86HW+1of748mPLUY+GtNhxYDB9jJ0QvamfgXQTe6HydOmYsqXBur6fiwEtUtn2oWqZCEecYNUY1ULexQtyVDVjGk92GcUC2A0mt0CypyLIgWtIy1XO86XWv+fuitHwrFHPu5Auyu8VB8khz5rehYBFwwiT3xVG+x/6RMOrI1EBDh01w2oYJEEbblOuE5fRufcrw4uXQwRRbH+TI49CzVF+kswljvoe+VqDxK4YDrMkUzIycL8p4ujG3IdRcQlYeuo+OflUZBDt4KnBcVT3lXgriured93iNH1T0ag976ZyaNrMJtTK+3bTLLiNs13SClUsOEbZy71cBtPgkESapzqr8/utjK9d5z2DKp/vfEbFXcdaD7LGrq6j4y9Aq9FmSXBPis0zcSEkYQ5S+tBWJ+X5vlEWOe1JpXfdQciZ8swiDGlA4Ws8i8fYqGc7hSnAgXEXRYEMbB/Dwvixlt5ERdvzoddYG0HGMHyWUQSGhjR7c6HUAXk4r572EYszymAUjDA4sMwuJyi7Q4u6wF36gIzoVlDO0rON/I1F8/no3P7wpObC1bjwZ/UuBoDdzaxB98kp0cnJKzyPnrBUhZR4rdi/CjTlfkOFu9lWismhFRYY2C2NBmmsoaiXZCZgtBxuurZ3allyxZWLS4hKjEF78j4b8Rq6viDWjL5+Gc3/EOKqbHqAz5f8rP3hfQmJ913FxJzG5RRhQKzzldmKZzD5DVMH6XV4Ctg37a0BFfWuEw287yYqKPGQ6KxCxeYz2RWGtk9lSdhfPDCrW46B0YeMeMf2dHIAPXOeb1rP6ORnDIthx0fD+4dPLcQ5LwOXWjCpwAAACK7v99S+SxJ5SOhMcZrPfJBqNXT5EjLj7V2Ya0KtmHsagrV3gga/qWu4Z+vmfz0jKrBvbTnZP3zEpDCMbI3mZl6ogoZwcCIchk9ORnFNhhicjA72iDP6m7DHorbspMoHsOPvLG0L05/hRA0ggowPtxft9ScrOBXZIIBQNrwsokTc+ko2zD5IfjJxzVmfBE0AiIkf9wbmtigorsv6kyG+QtRO45/90ijmfn+0lHZE5wgbiSNqEa4YM94OxX5jaCQvHXsfSIaWMTvGoOBBGOxNqdKjkTSraN8CZuBaG1o3B/n672Yi80EEgViW5nyPPOoSgEg6uXi/uIyzqOPl6qtFjnjxUm+v1Aj+b/kzZYG2pkxOmOD3bSlExwRq3gQ6tcfZ9LWk89VCCh2HI9VwHA3i+Xp1f/iFh58f7pOhYCAWXLHRQ1nPAhK7HO7N1gpfZcXppFb6XtpF/GU9Dy3UHw8KwYnMcuDI4dbmsfIRKJ3zLk9XqdSMFS1Qp0Mn8kLd2zrn1FF4ogWmctNtRBPbRlLAOC/0IIt0kn/4BvFsWEJfrHpzJPLAaGkchVkbdtZihHCEUG12P44VSzxRRX5FwzmjpzUR0w/IHgkd8zOvAQPQRVAXc8uGX2RH7ZShVE+HRgna5IWnwCR8mg+6GNOlhqcXBOLl/NtVlVU3QmTp604lQ+LNvuPwWZrpVvUyVX0e76AoMNmKvZpFlohXXG79jiKJ2uFkG5oHWlSxkhHiiee0r0P9Wc8iFjGEWpyazKy9moBLkqfcZ7K5uUN4aHL1RzM0gLK/BXz4hZhfY1+1ZIfEP3YvHbMXzIHsIoDhHKQ/AtkANvrSr6PYBHQMXs6TVb67Ogl53EOT6Y3l5B8sfMoA7jmjfZ+EQM2ViqzA2jYob/hCG8U5j2N70zo6nYPjzUcMEJYch0mRlFxXBg/CHibMGAkDUVg1wOhQJ/vm5cTi+hVSBMplq4VnGDDUJWp96VK10K9m+YqQb22uTNgcODMPUPYztx0C7/Z04/UoodZ4FvI6UvII6NTy3Yl3uXQc1fgPP0nb/wNIzODsWlzMtTErpI8rBGL+pWM7BkOhz6f34Ca3dtKmRC3jk8AAxFBi3pH9jJEtpxlg0Wt6XadOMs/QuI2BcVJOwhPTwgmwZqpli4WqTYRd4tTjoihU6/b0vhuJS1vgXWZvCX/Tw43i1+C6pMwD0lHLnT1pSrueFxeeGHB/6dhStcOF5Ho2sPPhfpJaQ5pz69g7zY5XWTVFu/bVVf9MEhxK3TEMK3a1u2UEW9PHPcIhMX317nsGmngbFnPBz7pNDfAqBssuuDLg1AwaJaVKKwPvWMdMI2fGL6mGx410AjdEZ4IdDE1rgRAsuSzbg7+/HgSROK+eQw6Ht8p8avDO9raeY8tKi/vVQyNUqP1mCLBZdEDCuh/Y/lh3+lIkPJk5+psFVVfnMUbhyjjM8E+XLjfHyIPoWSQEf2/uHXrfXbvpOzqSTCIGItoo2rQRQTdlnvP1dqKEVvjzCXlY2kpN8Mmy37h4zCYcnNU7A9vGoFgGL2ExL0o/WJWyFmKlPwzy4R9XSEf5cjwowMcR7uv6VkhyNA0bcvXShpeg/NzNC37ampEyl8un75nFqiAUDWTmph8VjgNdsA0ktT31RV/Vi5Rxa+nPfoSZnSnuiBhY+yOneO5WGmfWuLOzfKQh2mkws5eDkmvVP8m38eaPWPASR++6k5h/L1GLgxAAAAAuIfE9iK7BnoSOtQUP4eK+ppz/ZCljY7TtN3WxyGiytRzkN+XUCs56sueA8ona7EgB39mhRitXwItojIjblmEpPzkAraq56h6fYIJnOD0wZdkSYeEWanx/om4XX8eF+QEScIiKljUkOVxNcvHeZJ8nK75hjXu8nv8fPc1Oig2g0n0AY7TeRwcSmuE+oheDLXfk08IGQaSVhaWlrrX1MicdNh25b317FVpB+9gmx6/2C9B07cPYJZgr07eoBQ83g1rwautrwRBVLRXyDkqL00ubgHlgix7re5t26ZBRddClzoE+cMM6I7mNULsfVUIOLjSrAd0hCAylm7c4BinKdGvIPhfTQkO3eFvgokRDsaXa6DJCJP9VvxILuVI0qL91v6hs9vvz3TScRtCNZArTwumIk4+aZ+UI5nvsmlh5BFwXtyHG3Y9QHBjVv8LugqZK7HM9XK5mXLnWZnsSywHhAIxLdP/YvPsZCQrBAtD+ac1P1Vcon8kdJDp8t4hknbz4o+xI8Bhd45yTYptYGW56bK1NXp9olyA3mbBmX5jaZa2A4Z7our5yaolYsJei1ACerrE1NBJlgkH/yEWrcZN+6T+053CxG+rldQwAnhBY+tImLyDigBvl3AI/LW60s4C5+WsLbApf9MDhFSHopVVtibBPOY8QX1KRwuTRnHQVllv6SRQ8ZjojLylw/I1WloytPTcZAxleWZfTUawedSChHMQuGS5Wq1jAqe9B8q2bAbe+Dy8XAvMOR/+jZsexExND4bD3jBT45NOdEQaHlhe2ZBao1+aITF47bDgyhgi6GN5ga4PuYTImh3Dg6hEgxDdShnn86auxyG8CymkkIVAFLB7c9u82gnNGPFeVWsvREmxsYgQCrk3+D48sWM5VEzb6EiM30jmhitqLHm4+putl5yDDCI+sihTXl8yTqDznIaH3yGOxdKy69q9bQmfSGa1NaCJyKiAaSeKui8qreIg9VsL/dtw5rWGDRP4FtKTuwvphRnosRJG3PZ9fl2+lu3gfdShTLr+u7K7/KxhM8lUeJyOrf72jcYYUMArkVHMxzN7/rWIeAVrde0y18aXfNkgJYk9NsFLV7VUCfWB3T7M3UMX7Df89qhu+gAAAC6gCEdu0VKVToVRzwMxf/rlK/pQjSfaKApjwtpTP3sp0FaIP4KakWTHWpRhugBHiuEWPNJfYEGmIAIZaUCHZGa1aXTQfmJ4+BQwP6bSH1nsp4hvy4epbi+xJaOVLD7Q1STNYP6HStPbet1YaPsiMYLHpJM/GIKbtdl2HMOxSpYp9r+i0Biq1yCih4cLg/lwqDrKESQ+Ne7MO1UlLTRT3XByiNi1hiz+Fq5MRzhwYqMZQXbLOf+6UJg0M+tIzWAY4hZAGIN4bIafsMMnD52+oadrmxSdDYhnjYtbeOjVpykPZMYJAWl20gTQNJI9di0AWRshxYdN6wBhaUjNAa2ZiEg9aqwFg4fbSM/KLaoUIEw9TdlK7nCH/P4SETbAFBNLc3567KeqaQ9kiKTYpp435hAtzS+NWzIXFXzO+it0sVToBKVFm8H428vDJFGg72+Byh1CrBQL3td+n8utMLlFVnprYQFn7ZNT8FT4yR1MgMdNORVCJyZniuRTY9iv6c3UKNh0aqWhmqU5xen04IoYrwjm8ijrd+exmaVXJx2nSAb2ASFHkC5qZLG/afz/lUoQBG46SihkQCYJGbjUjaBtCZGiiQqADF3lLqOKYafCxYYB9HBzt6KUz05Kmr56eFI7QqE2q2EBCfjCHMqpoqAhSPJCSzu41PpMTzX+7mHPTsWGv4EZ0NMFj/bl8AF5vbCR7UFfKcpISr1ks9tPW+RQO5q59ISeC2kq+VTmr/5eS9P/yRrapaufaoBT8leyX2OJdfOdtwIYBXPzQBqBSZ/mG+kV6dnYr0cdi/0lkbcCCkvMzfeI5FZ6S+Yvv+1nAiyZUhb2dbuph1NwgjisCD/juF7bBQPDMmHGHvpJ4FM8bbGQe7fDYc2XxAebutMQSyGWrnIt0CKykOBTt4ACrbPfl3b3notJ/aVA4yZwNhIrZDpETfzb37mmpXk0eV8fjH7WxT7sIt9+pQSCrsBwajr3289JUhAQXWaqMg5YxTBVNDaXPJD5/Ee6RsRW1rSrRapB22RyE1P1Z/clj4g1xRm0WDEA4Qi1lbrNN+otquvs5y6rDQGoDFo+ctKe9tjiX/dNtbsRIy9oQnWoRyCgP2SfgUHXQTK5UNepcAAAAAJHaiejep1EwDC1v6E+Jj/qtMa/OH2oj5BYZPrm9JC2mv7/up/oSGYF6x52S0D4nJrcLs7v0Torxg2MpaYBaOvEsfRSZhGDbM2ekLb/SNjmy/ptutn9kw0TGBYgAAAAACnBezMt2gsfTqUdVvBvdI0J+iioTPpBQfFhA7pyWVulT8z4MfDG/RBrXqGmHd7SeIILu+1kGjxOZrPPAHhzQ2GeEfyD+YwQuKzDHZycewra+9OrxMnjKw/O+rRRJsLK1+NGmI07AbGF9wNR53Wi+nP0RzPcR+YcYodO2jU5FYuuVia3pCecSZvcuqWfKVKcU+HnoJjvCf6idaewFOkBwPjw+xizMhquNlzmQLL+rllLIHlKPNuUhtlAVRKhFAQ1kxjpVD6E8PbPNFGg8MuzsxNj+7uCbMpLzSHdCubhTuOlBlPaN+Co7d4fWzaKI1gTPfY4CmPBLYH3EBy5mt1fejYzOPIvz4LNKNrw2KyOGz9nAfqNTtyAF2rszHpH/bJbP6MNmcThUYV5WOf34PUzStUCX+7s9/JpAZ1xkEMnqLyqY98hBLdS6vvo3O+sAx6/h/W5Vb3iq/EPghnf6A1MDeEBpuEiSdQxgYYQ3BCx/iZYhAbrNJ9aSxy5teTnas2sdTdWq0h0sIWevhsr+CERhUkdUwL/Wrv6vz/4Ujiic4t4HAFImU7ucVQcEN/rthRh0bGPXyBF40wNZLwyInTPlalOiKZo1sMCtkokBX2goCxCYP2sF+NeBkPrash63daI3+SeEsR9qGaugMtshb6cYpZCfpLJdUuIyl1vzm+RTrIGjZFcROBqCNw1EYuugKXa8w0wRWm9Zw9qsJ0PMK+qD6s2SgsolDrSrnCe7GCX83Qmz4WzR0OKpwPmvmwTe5EwMkcNG4IZ0MDNpoC2hIG79v4O9yfnx7VI3dV9joPSVM8ljjeQqFHOuTHpnE26o2BNDJ9tjWp9qukinjFYFT1IZrOFFXzYWlPIC297IQqTr0FRZzVKgEiQsVwTRBNn7hJQKL0BgAIm5q3y+Gy8WzZ/tvuqQZUuXkdRAfZPdYztThC0dAzsRUgbgKNpXJmLD9ioL0xkA8jUnuJs3p6Nm/ErCooiFC7buNzymWALfpRx7X0ksZu4cUKLIofnzfMKaD7UVv3Ozq2yNYE0FCnacbCJnVYXzIj/d41Sl0krb7dQNonXguPB/B9Vtce9NfgFBwvfLWOX5Zh4+IS7ukRkOuEhR589eJ2068a+pc4udDRjgCORNa66nt068O2dar3uZNUWklghnK00BJH+azJAAAAAcFe+SuClco3mdAkP7V6OhA5QJej+3RjYmqB9NWSd2pTiZHpD6WX+tFLTTjHYV2KcMzvevcViRQzzLF1QvgbNe6pFn1WMUeCXukoRaFgUA1tsO4wZiaLLK902GeMtMOGWeVyYcllGP0IeOWuRegEeAs5wepx4E8AcOmol9PGIGlBzJMNIVhkU4T+lZilxXJR/trZrj9ZzbwIUi9jyhH+ewa1iTcY7BtR1Kb7BCDnInIzMFvH0xyK24QvpritfulP56IZ8gQaJSWIxS0I2xpmToK6Trk9YEAsWb46TRdKRINU9zmupiyr2/0Ts5nDeAlEFdZKqHrG1+9Rx5YgTAAAAAfYdoCBamD3S6VAkKeTDTm+Yy7cIJDQMOBxgHQCK8DvgbLcwCAainY8scKN7+81gn4by8Y7KtL/dVhb66fWG6K+yO7AzkK/Vg+fA63EjBzOrGv8ekTQzWGacpgTe7EsUmAtb29SJU1qj8U5Sq0kGeKs2T+IMxAukFJ4JgvIzXgtKFm/pU6QzfY1nBT2TDBdmPLYhFVkk1Sbc2RRVGcaQUmeIkMraPRP+QujX7sWDvwQ1BJdMB3fvoi6sXFjIg/kWeb4HkneiGz/iNwQz5Ud8QJDtx2JYVDzvRCnyfPsvWDBooSI7sGa3kLnzoqFNhUNR/N3LUxVklqB1aQWsMV1dcyivSDCim6n90zXh+CArNByZPB5hy80kl7bJlJR7sQ7SbjwIO5ZUbTRX7Avtkmhc2qZqwJ56RdQt3V2NYeQJE1dn0p1YFjOtwtIY7LdYjRjbKdNmgHNFEnHEcaEDhNyeRRM0GSAjiGbfC/uG+oeBGfnNIui50bsyxZwFkUIJfbrISQDJgVypmkx3N8uyvh4boF1LPUDy8oBzs4UBAeAz2uSxlg7z0mNaAWKeezvAFY1Iv5KAF4+FiIUGZXbD9LbPwmDkji1b4Z5qN8sTSCATa73MuDPUtC2klfQVvIx2GQFhnaT8NSfxQ5LRziFfvpYzQd67sppTt+QEQhjk3KFKZPScyNGpub1Uopia6dOMqdNxadMqzKDXwZyQpuAkUMYHt5PxUrI9U3IFtZDHcUJsgW9JWAytHwYVp/bed4PTPwQiqMY2nVFUpRHXKgqDuYDh07EXr2CQqtyL2rSRcxaJSFsqRG6U16eB06Z9epsdOH9gseDfhDghbgAAAAAERcDACVGFjy4RCE2o6yIA0lREpnENRs2pwEA7OvrX8jsNxmQnY5pbM7WnAPuDrDLIV7WnWXHsueG9p0PNlComn5tFPCCfsJeq/euS41sRK16P42uA9KguwXNrPnuZN8kzzWWU6DEDhUNq9gOwM0RfifVD270l02ipg4H4NX7vjg7niNY0jKK8bBX5C4y3j1a8/y32Ku3X4R3cG5NZpzJ811fdbWmuZqIq+x7x26DiiPGrezsVExwT/g8Pe0iYRrQCvnLYePJSNT63G+R2xqHAT7fNNU7H1FvQKTHP7GsY2efSXJmhqHQ98V6j1W8PTrG1VvZiwhwHHXLIEef320VRDzG76GG3grp6MVp7VKHVfCWEDzXEGCsM4fXBpWAXBPHG4Cp1GKaR1JlgDA7X7cuxR7bIZbvHUu58WW167WYeQL0OLWntA1iIFFKzMJ2PqLekaKWuG8he95QrLUvkllGc/TZjYNshp6KPEdCHKwEQqo5D+T5QjXHxGJZTYQrnzSv91zwANfBbrCPIb889oS1znx1NplO1W3LjP2g9MzYYMhn2DDoJQCHBfLhRZlD9uNBh77VnZGq9bcBXG5JwNJzvJAs9P/dOlPLZQl3PkF36Gpy82DZZOJXmnAnANptPcgPRUI9oojLQ/PdgesrqN29cN/LyLe9RyqcWg+NcSOwvjxQaNBllYc9mv+XNWk8lQOusEGaj2T9fATfE6PgB3OUxwpkB6uhqH67jB0dMKcmCqtTEn80vymB/EGGZ7r1wCu3v/r1Ihei51eSs2ssQ2Cth7YnTROUoVG9YxfI3siTwYzklsvm9eJxTscbaK9xu1xLq+4QWxkb6hS5oKw3UiHfojbg6+8rg9UWw97mIcz7JXNiFIrLvDCDE+j75NMasrUC5haIOmahZbwL/5KTDgSPTaW/6GmNw6mOvIw+jnKgSP3hB8dJ3L0XL8nxQ3r07qA9Lmjt4owXDoZILPm3QuVkA6HkWecAW0WCgxto1ipu4vJVZKwgrOdPid5mYLq2JlHWHGzvMQDWdXegbLJzoO7zf50zRJxgn9EAV4QD7OpoD11BtMx4pQCmA7u3vHTNTUI/toyFg6Xoy7fTE8bhzp+U6f1bZm9RMeaca+vLuSKUEuh1qTC7uhzs8ush4tCo4tK8dBiJwve+orPnrxzgkFYf05NO1Dv/T+CEEjQItkFLNJ3QgwxOItAvkdJd5kYkqmZGRsd2bo/2HFnmmBQZVMy9I0ZviTpqrqJ0TJmjzTRUjiqjxukmzC9GY4vcsjJUOznMY0eh/q1HZ5Pr51s/JKXhI9FSWpijDWClw8gNjUDoOt4J0EyqPUe/7flLRQDhrBT5HHH9niF920dOiDOlB2ezpOjlnBjNiG9m5sRXgbqtrsEH6ZNOV+iD4d8SxjNwu6l2x6j7LT8yIgklUCSWmq7ca7nsuRaOs1w1IuUDvr9BML+gg+DkUf+e/9p42aIUaPL6jXhuIeFiOaJ4jUpODYL0W530xTZ7J91/EF5ezb6PjFZbRYlskaJaXcB8YdYMErn1Js5KsQkBcKh6Ps7vh6PcAKeXt/Q+HLpRunCVVK8k15bg8M/3ubN4yu6PvdhZLvupBO4F9MJiuEKuF0rZlJB1Kz9gbc94hUFHJ5SSDu7bbagwa2sYp74rOFHpBdy5WyydYzaMnQe3OXqmGYEaDo5ku2CkEPBorEwuDX0r3tzifDXnPopGbxyUkndMJnSlpOc2AYwhaNb2yuhGDKS/14K4pUn+YecRg0VJgTKMJVRNGa/dJNClmulP9coF1KwcOlS8mSa4OvLszXFeFa0ZCtmzrGc4deDuWsoCPPLEE3lX9eeECzqPz4NzsYPNs01xBcla20Gc3L9+FuXahuLymUilk7zKWrA65Em5BnwHu8l0O+WwNbXkTyMzvBjCHa6nZW1TsFJ8JuYbHOwFMDyGtTP+ew5yrUgGEX+kyicFG5D0OBEt+VNXnOYzWwD5nMCtmpng9nvF+zpzNVWCWTvZtkSwQQgM+Ni7kaT+cI7PtNyRjUjRvr1TKEkQNMQS/gvdI9Lx0F3q7NxEg9w4qMktjZNbo1xQ3fPis3a12nMnj3IsC5X7X5PW2A6k8bQbTZfbUknjqQCQTTiZVqMME/f0k4TwbW+OrIgIVcKu+En8QXuh1h4A0rsb9HjqGWcT5acxpQr6oMMMot3UsdkZVi2JGSvbxStq4magEKE5jpPKRrPQFA1Tvb+XBAt1xKhnGKYLTRqRGQrYLwe8UQZ/6bkbMH8j52wmFBhI6e8BApOfiTFKQ5f4cO7MjHhLIYiUT/a7aveEKwXlW/dbpeglMXQSB0I5+OhXhFArOp+KGan+SeauS8uMBGpkefENOYzij8mkElmy+RGuQlv8+elb7NLMgznrrv7xVCLXbbhWCyJG5t81CnDVtI9uuAAAA6dMrapiuuOcrGKIgVuD9Vo94/YWxpu1GiDcfw1gYh+1SfWDtCfYokf5a1FXZoocWAxvryqijo61CfQO07ad43Pi0nPzLmu/mz5guG6x+NFrfJkBNB0Y1+P9MbPXXFP8g95ytJSOwL6Q5uDjEwXPZWOqip7ElGLFFNXTq4HV6BT7stdzoBCvek/jIQE3+bcn8sZY4MIKoQ6JsfvkarB6MTvkVWAKpliESfClUc31CxYbF9MIOQd/hlYIooLdBdI9Vw4peevYgfvcOYO6vUN3j8fWlL9uv5J/egHUEDrY9MsxnzpU1Gn1UEvJtOURGOqQ53eruGBAXaj0wR4vTUkv6y0mek/etS/AUTwOA1TOsebedDkBjqHGLBEUlmtoOwuD3pDQBVqLZhIuUw4evnWz4k1YOYroscRjROg3Y1QMSEpcM+z8Ybym1db0WRyI2Cen1Z6RYAXdWSqv+NB1rpj9oe18zKwjqtBMh4uMTqUAv7lVn+JUAPZlP07iv/5uhE5dFvecqRR9/iZUbd2vubYRFzhCF89kAuGj6Bf8H6+2DRj7+X2Tz/KeRZ6VyOHnHmRpuH2HxNCTmQRyJsp2LxVjztfx4J3bVd7HTr46jPMPpXkPpnu+YwSq8NQtZKg1BysGQNgB9YbHixn5b+gY1uoJjxXWA8DbFAk1riibJ6zYa4W0JaOecR5ILF7z5TIGRvFiHBRJuBVfwbmyJjl74Zc7HRynwQeUNNxvmBPx4DvLjiSQouatGr8DwRC2J82U8QwjcbXhdBn+/jfJH8laxVx9GCP1VJDMApEtlH7/QvxUdEhLm5A7Al9sOMFOnG1ETXYBhj3nIR7BH+mR0Xx4aPT6c8EDlC4KJDQYjO2yxMw24OMXoGnZJy5m/S4wMG5Eraux3eHFvJU5FqwPnBIRvCTnOS4NJC4MONVllbQbhPHdbLXIwFwmfuDgh9kfcM6grlQfrKQFR5P9tXIIetCiofTtmhOnibKT83FrHutaczh7toBVCghdEvfuzy8+N2qy+v5cH7RB2BQ4JB02MV5LX6sjQGvAG8yOKMm6t5tE1w9Gm6W0OThr57JDb7KprB48slT7v6vGJ1JMWvTncT4DsmzONgDPELpbFAwOJk/j/D/M4ktsIVoPHuB6mSz5iboEJWgcxHkA5bIxKLaueVP2sDM/ML43wR9+Ku1pXIReCr7gYOGqba4CEzojTkpFnMWZhtE6OBMcNDDJ4PzxWfK0kPcMPeVWGgo7wKopV0WdoMtYnxdKGU3bbdSdNOjra2TvBa0bfXJcyMUnfD5sJPAplDQ8ugH6jHV4aHEzmYmEe25hqbd+bsVrdUPnowiplbhlvvFkOYZ+s1DRr4kUxys6upLPAfRU6ohpyvSJ1FX+QZ4VTu+AzDXuBtTxplYpMplvbUMWsihxGSNkeWNzuKKZdH+7av9hquI19AKNV1l+jtNb8WGzORk/DRbXXQrm3+wWjcuPTb8uWQnnMM/75m8XGs5xC6Tcg59r+GsgAYwXOLg8fx8pGz+/FAKcCy8cvQZCCOsGJBOcmHkvpKtSMLUBsoUzT3sxRbLj4mm1p7m7tyXs9Mp346nimi92TepM08wT715RwxYAc0ao6/WPNIvLNVLg3aY2n6oOj++lnywAAAAulPOXZXAk0MnSLoDHWUvr8Omd32Vpke/FH5w3Q1n8UcBYbFNL8OjrRimPPYFGpzmJPNURJtGStJAl+szsCWzrgKjhpdCzjeze82CSDickELEmRqmopRYrfWjq7CNwZ+FkC8yHCOFkQqtXyUtRRb4g/OYjb7JqKLT7S0uJVVjRSBeh1gAimI7afvIuWofLQJS+Y3btC5ylzC7AXjiTGN5uD6VQewC3WQsRKy5L7Tm7KwsG8DoI8tDvSzmyUS7eWjQT/ekWaIBbVOvwA2LE60F3IXSr1dXkXq00awO2gzJ8dGiN9solD134jhby/aR/2esVrs/VplBHLeyaNvlDK3MQ+sA7pXfJNDVNMDl1y6x/JzHIT45VILefdUSJpvcYmnJ+Xh8nrjXnYs7Gfb+X7WfOjuraLG6zedgV/WlNs7t55/BUj5MB6d2uo5b9NWu0vIlLhDUe0j/dxWsX2NgnvtJMS8hIL7Mu0rLhhFqs5Y+ehxYBuLZo/Wi4fPYfIS3K87lnxXLlXpvaI14BXYkqUvyWiWGeBCFSqZMUY+oa6s4ax1BMD65JgOcyv92SQyecgBRNyMBji6OLEnLWtIyDe3IPFIPFHD6S6w3h3P5e7Frwq9PXOnNIifCT3LDaEFg82iIRq0pRTIdQpz0gmWSAthaY1nsRHgkUI/rTIWRLrAdQq0UAeGuUoMghIVFuCiHHoSV3nSey7ilMtcs6/xUN121qbTDTsDpmLD2rqId/NSvvJnaIoiF8VL6t8/ty9V97LiI1HvlHTGd9SduEy+4xh1WNJkFYPlvGZwkTNSiCIMp5OW0VldmTY1YPBZ7wbgWXwWS50ttep6T+65DtDinQ7ZL4v3oBhVPq0Z9t45z1ejo1f5R8qO5qyUMZIDKYSedgKyv1YHHXeXV+YaKFMKLIzJIPuwKcnYQxEEcfh6s1wCbyE/xFJPdH+EBHeRlu3jTA8WnhsFHp6HIlj8MCk+T1Z0fWIdY6UJCJYxlFT9K0LESqmavIy6AzHe4JF91pSSYHcUtzT3GJIq5qLGBUylf5Kv5FAi8E3w7UwC2Gs8EnMc845LNWcWjwsoylGRdTqNqXXjoiaOBEjabQNCZUV0R68oOTHlh4OeXA65XfUGwAAAADTP2cEh9QSVRqL6a6RaNdJxRAFolwiyL+nlzzSBLlM7PdNqIQpNImgsMxOaauLACiLSGTqLnljBidRnoudb3OAsB3H2q06ytm5ut6BhoPHgZq4VSvd1+t6NB1s1aR0X6DTTovJs4vnNa3QApY4Zkj0LRw2umSD2zBqAOdu3fAEWy7QJMf1348KYg96ttXdTnLyy9ym4ApYMRKHmb6Hq5S0EAy+yzp5FSEvFNEZEfQZ3CTp0wEq0PCPTd6KzE/WlD408CbXyca+WNL2EDS+8Bru92nuLjwdT8Lbz7aWr42CpJNPBtWGi1KCE5v4YXMRi/3f5Gfq7BTEmyPZjNgToQLR3HEDUYrO3lCZiuBgoAH8AnJ9wCGb9hg6T/k9RYOvrWfWudtYS5mJzzCS4EX7seqTpXmeNhfs+ULjp9OhRwuqxU2xnD0ihDjpdtDqXSm1ctjtuxtqKQzJuXHTpjAyD1V3JDd1zUComS48QTraaP7pGF2HBnoOKEqlo7hSvYEcr/OZT+U6JlguFnnpi2d6uGNtfCWaw3SaETLDVmuJjdMlNCeQOGTqeOvUICwHvaSh3mf/pp4Xq4neroiiBVD6kSJPLtgFJ829f7tEx4NjEXvzCcIPxUE9FbK6hCtmY8DikePMprnJZamsspRDYOW4j5JMt6ho5W36bqYYJaQ7E3q9VQ4D/7zQ4JYWmImSkYVQBnUWNkAohv8BYpu7q5l/oWLglkNuWKG9wBJn0Kooa0GPAkWl50/xdT5kbc59JVVipvn3HP5gUulpIWEsmdDnpnKWNjbZ0QhNRsrDy5EsLFqXSNYPaQy7IPdOGQiHvQPc05cRrs6/+8G1BrK/sCznqCBSXlllSUtbFZWBN1Sv7CgGVeTkwxRmGfRn4S/S3jpYDNrirCUzf+ztSXmSGy1gNScZNEnT1/s2vEOCLAW99UXlO4b59uxPFELF+G+DgRuA1nlQI98l0+7u2/swwNIInW6gfVq1sVQUldNMjCeJa0/H7cMK81MSWP1sN8eTtuIUGqEecgicJ3YoKiajOcRk0IlQP7WUR1jsjfWeOeOTTl/4i0Fd/BgvPJXzZbJR0S102UX2U4rOD2NCe5CnV5Hk/csrtpSPDnlayJUmY59QgrWy2qChDWQ1sBJq1cd99Sw6Ms46waNr95sHPfhyhXGoMu4wpg4lG1PRkdR1v6O12tox0WyqhWF903MbqRIiNcx9pG1N91m0ZcBeXfhinUg368a/PhdfVEbr351sbeLibVm2YwC3rk9jidmPzO1PMuiVu0hmKvOa3iJaPPU2cgMzClxENqlLH/9QrO84gxACM7Mv4uJWLglzr0QmdRnkQ9yL8viEgtZ3L7CVNLOvR3AAyNNB6E1/m6NEhoy4NkLY7XijTUY6Oxr2jZj2oD+GDQoEnaiRh+a+e10OHSU7JFsPyx3Y9xtKPmYedXtC8U749y2K/vYXJDIzvV01xz7jrGTlVZ5vobglXw3j51OB5NNCXDHsLeDTC990Rg7k43eX/nIMLcwhMDbXQD0Apj94fx4kHMI62ays29mKVwpoMlM+oSaK/JH7N8SlzJ+wQAAAdb7qk57v9+upKZETZiRaiNJmj5aSZWoVLoK+/Zu9l7R8WycMk/g4x3yfL0VMvl+TSfMRwhc5qeMzt3mUprsWgQL+/m9OyLZXlTWE5WQoNnAzmQDNLMW+/MzZadJ5oKkmgSC2BSsBliQmFipPPMConQ5wukfqfU+23/kQC+XfyWZxm5o40wsALhYCOx378udgjsGV/dGMNAvaBzfjDvTPrObY/ovigrrD6KRC5w0xodjXztKelSBYnsD8QrupVc0rdyFnes84pUw4eNt8XV9shvk7yFKGsrEO1fLHWPu6KaIKZOhUDEqMmwHtgFhXTMGVH0mOc4EuvYiOFOb+iTlyfXi2hw8WgAlawI/QW8Cn75qbULLB3CvekbEEArH0j4jO8Vhs8wi7QV/Eo7+zFL+xs3UOvryg9JH1K0gkJRx1HNUe1vuteHjh6yliAC+59x5oTVDQ23LZbRTdvx/J2PIWNhzHZwd9K6NAYiIWAeKPJ0pVxLHTO/gWunJltv9+xk6zGZIqOboNglWDLQ8d6QggmCbLWAFAwxg+CfMvKPYh2OtAPNMrHsKfA/UcDJidfchYM7US6F+P1xKqGI4vUlXb3CYHjJi0WGBs35pa68oEx/LuA0oP10Up9B9sdU7prVAMwIIAMdteLz9Yg0HJ1X0GCXeuMtwbbfuXGmhcFNq+NoUqDqaEVItqcp5A5MqpmdU5Pg8YamLjvdmoH1pbfnf0SQ8hPeXHW8VvzB6ZXiNcEezBvLFIA3P4s7cfxuMk3mBLdbChMGRJzKKVMJFpSQ0YfsQbYDwa9gl5IB2hpdUefWKL8a8f+ICg+JegXlvyITUarRMBA39OLYeDsD3pDyPC0/5dOzuA5ErkjneXOAMyZcm9Voe6q7s88KHaa/aT6pDBKTB4/XrezZAZd10x6BH7BhgVHBBCQE/c0LMHbZPPLm1/Akv5gR0wyCGb4Lktsdp6wqCCMMEjmCkqjxM6263IQGq3COekJpdif3mBWBgBGYu/yuzf3ewQ9JNUFZLQCtlh/U+Zt6KEDNvxKFSrn/6fa3TTzNBOKk+BdtyOm2sHBM0DAFnm0Cl/CTUmSXmo+AIRs+th9bQDnuaL7jCLQlD8n40lI7kX9FOuCWEUZzSa+YDcBzZdwxXLKKVOfApLBjYZJvLhPoOI2fxZxzNrGMV1Ye3slFH7Poh1vp4BXAgNI69A7CD+mETAdVtM2ZgBxBTY1k68NmQ2qs0MvpaMkPXWSsZKP0cdf9Lq75f6eOoZaBbuQxuxnPsHWZeen+/cOkhObbETp/WOdPAqhfi4Z/BWuHRDkY/woRnHaJXp6mndS0S83rvAwD6QvWuC34DbbhUvCo8UPPChM1vXAo6rcHl2AWnKpNqglL5YrXczixVgA1GEFe7eZzbBzWfEzvykcuRGsjTlB0HusGuYCAo0O46IRZAOjdLLY8jjURNDjZ+E005zrhdYVl86RHUzw005vSZZ6VFvP5NMOFcp4LLn2IYk5/HSQGz/PBNBLYi5frL2LDNmnhb7SA6oVjv1z/XGpa2HtGlr8KI47/D+ORr9dgoPvrE2l423sx7UWZBWC2UC/VnhPcVWRncLd1hMh4jjYXJFu7prh878Rq2Ehx3HZwkyNGKlQmhTphvGLzq+ATIEz7d8PCrIhQN7xL5mpX3fzxjJDSktL8LtruyjtEP4UVjggiyHuqG1fssDT41W9PD2mN584DsoRMurz2pwDjDzK9UGQ/RjaeZlIxbCOEr7l46upvxdv8WnXGBokJnYKrTJYKTbXifIz//jG9y0AAGdnXcNoMYdHY7So/X0KkPmpnF5tVxyaEjdZegYEgXI0UPd6ZFkNyIVooAYfJGY7A/Iv1G7o7x0cnBHWhcL/hup+D/M6IQp1R9Dt5jlK6B/AiXM8LpLBu0YTjpNIbHEPv+bPOBgXLn8jZiWl0pFQcUTgsj6X93Zcm1MJW+KcKwTpNV+7sYoj9T9bwAiu+N5mlbo3VGX1fLNrQTXBL2Oda/2lswqRgSz26QF+w18buh97I/6k6We5Ed3EjaUehrXr8Vr8fhnV53FrpXHapJRalXT0HJGcCCTaACaAT72X7RDb233wFfrOFrWT87lSGU8w9wDrBSkgtYDJjcFBAnYCt9Qz1FlOIW5iGkLBNs4VAssYv8bGdjFxUlEKbl4AABP4mJo70gtvxzoeXi3Q5gyxle1hsLpSV/25mk9LMnpEo/09Yhj+JALXCPVUBj9hb0/5dqslvrze2El4AkOecC8EHCgKbE48MANeSE0q/SsiVa2fhZYy/PXdNdpWwY/hl3HKqcRQJWm4UCpcgxCF7LybEyU2KLberQdAGnON094RVwEbKCv9GqXF7ViEBIaJKh0CajGmYmqGEUDcqpRf9NXbZCTDR6Pa1mKagIPM5oZm1n0ThmLCtaNhZfL/xTAr1p04eyXHCN8fEgxqGzOqH5FqOosqQTU9yDvnLaKXa1rfTaG60SiueGv1cvExxPMbuCb7vMxkEbjpbQjWSj+5ooTEvOT6Zz/ylsyTGRftv32ekgz/e2nabMNHLSxPs0zZMEM8kpAO3TUvVAjyKnxuPJLRkfzvU1M04mUpqbYaJBa9s4qLRsS++nDhRXQcaGhApEuALkLDHtK3Ct3SKS0bM6A4hJNYFk4LLqN2Y230+ri6vA6x5N2L5oJZTftGwYPT2roHsexBSmcjA3oaxLo4IXI8po00Bmt2kePlTo+IIKhoHKrMOuBDt0MyWf9/sjBzm8nmq879X419XvC9OueW1FLDjeCg9fV9jxcrfKO2xXEAjQfFNgxTJMqVokFqn+2gQGwhUDwWRIFRyW7SJZpWca2HWzRJ7E7x8xS3Zk3GteIY1AX3xbEqGHcRtqDKuhqxdQ+Bn4ID9txf6n+E2dnp8Opk9clnbOKT2bneRYGmVGxdvDi9Kdn3Ukslu4XXCtS38bVYDSSTFRNORBwUb+0kBaG+d1bxPnWA6aSL/CgXsE1Jktk4qAFRw2C+Hc5ykjxoabWXhy/rJCe95B4lQyps+wjq2iHLU1rfXGl1xcvpQ3D33RySfcS31fA6KP33gvmlXMlMX0T5aiOlTqdTOPLAI8QDj+OTyuEwq7yY59NDsRSuvPqly7iIf4EfvNXmQ71Vo88DwYWLdU1e44WpYNHl1FjFbdV6UgUkC+03Px4nanzgpPKsJjz39FtYPnmR7LOvf4+QrPVx/kSKRWVpYTrFZ8KyHO+7Eh7VmO9h7+v/l5cWdsqqD3V1M9waAXjA0f9VTJVqlnaVlNjrI5gUcUVeF2wQxw5eyvbJsjjAjmnUfYcwjim/HvsZbDjlE+9gSExkoQe+nKg+X15n77kzaYesBltGAlD1V1/KaGcBm+ydMlDKIwa5Kkp2k9OF5/jSmloVIJaEfiLRbNopv3CRdFGOudiFrapzmtA/6f1XmcR+jDMrSHeWInz9lPBQzFjCDXunGXmTqn5jGJl2DwfLAUf4Dgu5ay0OHvXlrI2TAdsYk1UCbzMdmEIgI02I3jdzkqwrvAHchbGoinO6logTKUdwQZlqBhDXb5bfsmvbKBWvjZEwYu6vuk7Jklrq7TnHCUyEFL8t522F801Lfb7iZgReZeavKnofJarpo6JGYR5k8k4VlwDUAFtmLYZmU9LR41esyYUnBD0l6wbu2DwYrmNmuL435jh7CA/RWmtOTJ2hzDRU2EBAHFIs8AAAUJ2bkCLYntdJox1bBfIcPbVE563t9eTilIwaoEgGNHIyB4+AE2/vf+SmVhrwVKwqBC7776cif7IQ4jwEO+Sv/UCikDvKpjX5Vh4tkt8VqN7T+mHFO5No9DhXqAR1jZ0d1wi0do+csdgSLlt4g1VkcpYD3tl9Eqaa+RAHw6IsenkU2AxByX9/lAMpub5tJ6GfcVIJX1aTapBb2Ur5W721kNFduhI5rQMNCkJ0X4jep6JkV0IHS9I/AANxzhOJZ3I4UREk3X+sU/oDuMiyj4Zxo5i7do+qoNVFLeWAw8EOoSbcY8ZROiBWS1UQMSDMoYyikhXIIb2e2OsyzvyqgryHzEcCbzVVJLyP7mimQEExsKizm3cLNhjn1dtBzvAo83+IPNzInezEcDf+L4tE0dNqGuWn7iAe5Xv0BfEaw14Dqs8auKvYpVvkJOWNzGJCt0s2KSbTKKi2ZGNVnm//n1FFrhsjCpkJr/gfQ3BL/UWkljiEgp6Z8Wz5lGBk+PT1/Led55jVoapW5jEKRhooF9tAZ39uuEkem2iXDtGWLE/j2WFJldvvTPhb6hBnPtiUffad++Q0N2U1yQpVQGOYfbuFwXAxay6vn4tq4A5trhVFoPl9LG8WW6SREB+O/KVm4G5eSVtMBVxjv8M6uzXLIpMTCfh8y0Nktu3HS1V41loVOlvyHgWmfY4L4OG6cPaYkSYRJfSql+4q0+0pbMjOv0oU30LQjoxL5zzTVKMfCdmIKm4gW9C7IKRCcVyDxKz1cO9l6i2SZ6QrnLvjVTGcSjGxyLZ1OjPSc6PZh6xNfiKar2QCZjapB6Ns29xZic2pbgbHdXV9YLuNHSf/DgCsDhsbU/TELd/JlhKdrtc6MjtXXd00RQNd4DTQf1ZLrdDbqc+4RmrVYOR4ijp+4VhkHZWrCvd1884OoxIQ26ZZPtcFVuDoW5bPjW7U2QbJJtO4LqzULsDkXPgKuVo2EtEGQg/oPvR9+NszqOZv3mXzY7nxtwCCo6x+3TTjVBe+AnIs4kVds9xDO0W52V8B/O2GsiU4BFQjjfOiTriUByqYsuv8ndUxaXCiKdE3EbxjlocHKK2LzJyxpqL+ofrxRjZjze6UQrVRBuFfXeniG1VqjO2H8IcmWrQqjOGiAjcU+uLYYZi8YwBKjDaElQprd1CfeNCDJDWa0yVO8BkYsI4vwWjkFjRIsHkPrCSpcmIwRnpJbn/5SXwhbDQ5Xi7IYW7o16BMZKfFBkGPR3PfkSZFpUhV+AZI4wEesoIqIwKM4fMbd/VpEChlESZwXOHYeFqOPe5ynBph6lgJ9H+j6jrUtGAk8Ur39cFOv/F+tueeVoo9gP+vxJsGmXC1tJXFGPh/Kxyymswz4HEk1ri3G+W+tRkDQMDw81yVvAVuN/9HPCYdrrnzs2O9y1TbjHDixO8eADCrj+P2K5vNsV5WmDJw9R41IJqbqn1lMM+QfYUrY3yhX0RoWHsu54mMuS1HExlSvhYzNftsDKpk49kDISBY8gM05pDfDYQ8pjoFrBi0+uTqHi1uS8hwcu3bO9TXcXmLROqYFHS1ATkm8/h5efhBGpMOYsYro+0ifDQak4Jj+VHaHXyl0HSa6YhY82pEVq8JpbyoV0vC8fZkxUXLdYd/6ZJkOWoqB9lli+Z7khq/cMM23bigD6xIDcmkOCdIGKY9jAmojbhiTjFJMVibNOFtg09oa9H1o4QRJYmczV17m2s4SZdWpqHkJZVS5o4TANEckvqAafwAAAADn2OJLwr22pb9U1kgbo8jMXcQ98nZhxilJTA3UkuVIibfqS0K6DW4y1hA3W4LtU0sONs0RKMiDtWTgOlA0+MSoMJqbKvXYQe4OsYdexKlOON5oABDAvUFIxWGkuqZBvEpqShzdKAAAAAB9yf75n61qZ3iUUtAUyrkn5cUFOCT7594PAaUlQphztzV7myjOl4p6v5VddWgVxlI7oykXIuPnMKWn9L+D2pZwvndy3dW6+LV9MIdF6zvf42cOQNFbG1YNP0BNSfpbKHQCHCidBE1zysBArm21mAAAAAWJB3X4VAY9lQkhT6FxbaVFq/jE1WyArHipSqoXUikFvedjgq7XS7p+66DMC+hbIEweepXW+UZ4NtFpOFSmVrRhXc8v0C37GcndgAncPnzXZRlGdA6ZQ8/2VE6254CJJo4XV+2t+UjtdEGmqBsgPEp3Lg5H1H5xKgrWRLvjs7RBND+OsUZzo5woJEPDPkHTpStTcU4UsYbYxKkTqPx5ZXJlPUSwTLR+jPAIkULMt87w8/D3mccK+K0aMwl8di9cA9UauAyREjgRq1QspzlXaD8MWAjkkqaEhNq6uKT17e6uWSL3gwe9300Dnm4irnMXM3BI1NXSmeDlaHjUGBfVCM1AKcKnYQgqBKD3U4xAvJBcmdrK5yvePmlv/+xxX0Vwf+IjgDNL1vlCpBFeryKQUEhDeMVP8Fbh8bzX3kvlgLITRvDDSRQ9+4QwVL1lIUDlkltHB819l+lfERr87NiSH8vuju/PSD6XDRDczQMr/4fQfCTNZxPyh04VRtJtv5gJVbMTqxOIG+tk3wrB8w1otPVBuXJ+mL5OXjXp5clzdIuKknsmql0/g8KkN4fYV3nT4xVkbs1Y56vE3rx19sTjMgTECjQcSfO97XBAJWq3rH5cxqOEEWipJy5VAioGLe4RNk61kdmdpHmNQ+fnjZRYvVAINMrujALgDxiiMTOnqrKpGXoSAmhxA8X0qsk+VeIYUGRAi2pYtTysygAH0ypU18Ke/QLzNfMHLEYPbXJiEjTmAABfJhBU1CF/vupWiMPToTcC/MzJsODuSW1jES+OX3fZj3CsGplp18wxVQlewk29yQix+D0hWDLVNPePHYZ6GXRK0rZjbr7ol0aHLqjSbRKVcOoJnj5Rz2p7N0syVZHKTbju9fpCg5bXapa60OsRbWfWTL/jX78NMd0lYDqoik3x5a7cv+E789Wnof40mQ9jXsPnvOe/LiEerRmGNYyQc8LkOMnLdToithaog3fWOeO7mB9rzLtPQHH1vNMxMcEXSDxwBgYUYR1hefHpZbiP4D1pw0eG2ZAwpjVUnUrMsoCeZUde99rALLc4gNOQRhKUzNCwjU4TNd9PYuPjAExMNchcrQPVFUqY8ycAtlQn3l5UNJ3mnvBQimxEqe7vNeLWMiYbwv8hDdApwEEA0y2N717qMoMjZVMd67qYKQwxWpEkjjifQ/DErcd1M+P4exQ3Nl9M4EVGkbuIV8hC6eYQ4/wylmABQHuJLtpPxiQplsHg8NJoh4vpD2kDF1xodDFC/y1lJTj3dKTfJM0obvVvpFsAL3rfrrL84HTLelOyRzIiB4vpVZJ8rA84wv43DZvkUgIMxcbKWOoEPBKeAr78Bn520LbdnDEkFBIQ3jytTxRRaiYN/gLB2Vf5F1h3WMVhvO3H12iQaPwcWh/cqTSTBJtoGwIOTsFe4UukX4xfX+1K6WXZR/+O3G/hgspU+Ph/9YbW3FOi+N0T8ju7bikuHYxzcnBPz1eBMy98i6Blq7Iac3NFHVFXWmbgOh6BX6keK6jMJg69ji8N71q+KNdCYkkugqi/jiJYvSmkyqzsDZ8YnVU5HHWIpnRzhSZZ4nbkAyKnmv73pUrmWaMosQ+byara/Z9OSCQurZHLuDZbyNeuWNIRsjQVfSNOzdKIK+dQFBNocQliESgg/wCcKZwnwAsFrTf1wdDJsdabNtfWRT1FwxbsphJdT2b7wqlFjpJv8CyTkzAGf900gtDpU1sntMOv4SbEqgny/9AcFRRLTn4UPghekd9d7gHGEem7C8UrSIMGWiT1OoLUxGi0wI140auS2d2aixSqyDsZrB24Y75/7/E8Re42gWZo6qW3UIUZUbL52sYaulCDxZfP/iUaflnBNnXnir4gAAA0JzHiBTUoAAmX4RVrYP4ZOu8obmnv+NNO+1WJn917XNTdXAeD1CdIEdIO+sH2mKo3M/g9PIrojDVyV4sR40k1N3GNSUwf/Cf5HpsInB9won7z/41kjKfOTVMlCAIyrlnaCckTH7VwBgxB78Rf22XMcEcXEqtbAOLXCyKH//FgnglU0PZh9pIW5GmHXCyo/rB1DpkXwAAAAAAAGSsbTQ8z8RMRCMAAAAACqFp3BM06pRh5t2kmnxbS46igW1ujGaui2yd0/zKJetCiAWdRYKoE41oaxO6XDiUO625XrSUOkqSBYpaX5mzvGLJ5GWccWiP39IT0hwLgRKEumjTBIx6mBw/Kr9qD3oFT4Q1Ddg3+0LzgHy+NIqi065l6TBn4fju7SE7r+2NDVbgzb2NjENJTMXM1N7l4x3rqa+diGuWLGoOsuv+tuIMLwqx+b4xBc74fPRxsRMTJCwxpqW3XVU9AX5OxQdA6d8RpzcRQQS62wcKpgtQksWZK1cnhcrlodaDMatO+UDCGdNkgl7j7+8w9vhLQXglUrxmUqMAxoglC1xriNZMNp5L/UrPlChcq+novbu6C31CKwpY0FZzrQU5m8gbK7SP9sQZ5j857ctWP3XNLE1bGAurzF/54wA5MrhmHujvRPXRoJ09xqIL8pKELnHgskv97qWr9g2KbXi/4En6HTQVLWpmk0A70lGJWeCfLJqbzOiBYvn6tTFv+utOfhDvUtA+xurzELVQw1vkMteIt3FHkjxSlt/in5b1TLAwvKFhL9Q3doVPipTLEONaEd0zWMoMioqd+Vjlo1ih4dCZe7flc9UxMyH+tu6Qo6SgOhfloCzopu99MCOAqfWE3WwkSpnQyToRoPfeM9m9v7eLo56tIJCtX6MeSS9Luxis4ZjMMbCW0FA/LG08Wy7KX7Od75a3zszgbWgGOp06eDTk+8jWonwyv2sLVpHWxOsVz+wf9xNXIwGnqPnQv5ARs2ksRzvvux2w6ucJAdtm8Lflxv6qPJVaIlEyf2BUwKOMpg0rROvvj1HTtIcbcSDBTFnzdyovMI/ibLPjiAF1xnR+GI1LuMfeZhWuP4K2JoOdmnPU4nfJzHOkQlPCU5AKM1kS1F/Voyj+K1+qw/Kq07YctObuJER4cMALmBoRqQOipvv6JKuNvsBzQ/qLX8CPqlq2j9HeCAAAAAcABno3Cg2l6amjXzVRAt1RZPNtPr0vU8uTW+pqxP37lwlx3juO4Igr0bG4kaEM72akyP2Vy7WOAfmVD3tr7MicVud9mjjybYZO0YNJ5EpBc4pGf49MIolgwRT20d/I+fKeVjONTC4NG1JHf8lDt++bozpIgvfFB4EwukD4QAc9FeYks+KWyb2fHIoMiJimFSXJmrmSS2nkEDtyWlrA/H+vQMrfM5k7Hls/D/U/WivmWtkOH/I1hWNXJM3ES1XCFRZSzUNa5L4wjiLjuRinuN/U9T/9jSQygpmIWXMEThS3ZdxhP+/lPltmZJ2RxQ5iYC11TeJr9qv12TczvkTI1flo51eMN61eKaz+S1QKk24+vyF9W0t1ddjYKLnoolzCKRhwixtya81fanoALW7K+8N9qM+pDQdGsdWBd1UiAZfWzqynWfGtUvc5zOScrqsWqAQ6oMN8Opm2vv0zb+tso8IhYizJ/p+634wobivHTjcSUIeZfV8wC5L4NcI4sUKzOQjf541gn+5o0rEhHxa6Ch9mDBH2FY2ONCMGLjYkb78O4FLhtjHnYu3+xUcHWFCtI8DvcE9vXX1bphzJwX84dus8J8rQDNORtKgNJpgKD7XozxQUJ22BSFAgOAPDhzLrwIzuQcYVUraSXTrKQynL55AvH3YSx4shL95LizABAAsjcNAYhxiOI1LpFAgQNwiy775243a9M8/jvYEPG0sCjzf+GZ1Kj9WbT4eobm0PWdzTE+oQFHc1dTACHsCxo+MJq/263St1KyTzfyHXbGLtlrZPnaWDL6fMx0DyzDXk0lru1cTNlYWAQ+44sPVt4FcofsnfKuxOGZ0ExTrUNpg3uRbu4jeLsy46UUWW+9HMMECXq+19Ti9Yl463fKmp1DjET/k+u9jCTcXgD7/B2CgbRKIyoYY7VAo1K8X2enU2hl9WygFPMTuBNBuixwUj/5Cg6pRvY84lDAYMpOulso7pd5e2JnH9PH8EgpySiMSew5Mn347gFwUIZhGphX2SHHqhepWYYWxvz7hwF5L1zYaskIjiwHc24PsSk42hRjDvdu93vzooxXlXMkjMK+v+XuAKkDnV6Y33aBT6bdQuLnWSzInq0S0hW/RqSc4XbFNAr/SyuQChl0XsGVBuLh28YaTSLpSF3VOgAAAAa0TWsGDG/MRDL16LpyOYxS9SAqJveI+oM6DgQpRpCgL/E4eZhPG3pNp8UdEw6ngp9as2IqVsLiIizTS5i9P9BrrxtTxdg6qyGsxFjz9ESfyZG2BoJjgU/1DnHJgoVsxLuQ2Xi2TTVh+54mHduhcvHTRH4c65+YsYGN7ZY0t+t7C2TtNOpqZkf39Cb04T5C6HPXV4eiEnFkdOBZtd9mwIDPIZe+1Kr8bZerjWoUuzFFOTGsa9Jr0YWP/sW41PA2A1BGr9Uyc18LUSNk3TRhUr5AcDVIkmyauJNgrOzG12xRuw+x3sQxcz8hBK+aN4mx9jksR/uq7CS4UhkaKb9IBgIj4yVZeoCDKYSfc5gEB4KzJ2o8ZfU/hgVMFwvO/RFxH5Ou2MrnLBbHF5yaquQpeFyji8MQST/8aum6hBCZdufCfnsfRSnkUCjbxFm5+CfmbxZgyKChgHJi0mJqigCPdoWTgvDJt/D1owFlgmrC+rIyBugqMY2nQ9VwW1cwlSdfjWwrMlNICMX/eVI5yZ1m9K4ezeWSJe7SzcWitDcAXpe/6DM0J2Ct4j6daNyV5wC+tU+/rmVMjympoDWdgDXXMXDaQqF3qaPg8RXciEAVfL51QG+wIL4qfZApetQkacGEI4vA7UtJzx8+HheOvduLRi2IhXaWGX0ZAsl/5G8lDKnztgqDO26gF8SRr3i9fQredVdJjLv55456J461H84G3iRgOPXmXSV1RQsZj8ay933nB15EG882cx4rgDUI5PXO1U1XKqL7WnXhs6ZRcGPAZrT5iS1j+luUqmggRzTKZhaF2tNxcUn5nzrWGp4w5DaI+pXZOZRmfyxGORoWscwwQSJPJoVRBtkbUpRENMId7cD9z861X9lQTvfP0zKosBYzqZBM7/EIUbmmHmn9LwOndGSWkXhXptqywi/+VudDcYHpJ92LqYUpsKCl6a5pf2lEbxq1GjW/mDlSW6HYgYZjuXM5ngWwkAZ4lvpnbjsIaQWTEJLNE77MCJ1WS8f/Rp75/nyBrOgxVNSyhhIyvdtMU+WHy9a60tle0Zs3zcEvIhQc4awPJvvCsh2wwTIyS0A1VV9ncRnX5RaiudRLwbqUUxUGUCmMIlHfC99D6h3LhUIYSB9NPdYoz/N94ydNgrQCwA+bx03FlaJ2Gw6TKi2cb6cUVr5fYQNEe+fk5IC8i65vKD18EpvG9eJUmYhpvUCZurxAjy5YL1/t3HkboewLOEjNdusbdMvIs6THmDz9oORgxWtGVUtKr9UyeeBQoRxaEnVVr5oK8ZHhoUMxEPkKyAAAAALXZpwX2sjonP+JYVdeh3WrWLQk5W1NmmrHJ2CpaAzXKz/v9iPDsSzcRA8tJJYtY2ZcMCzXfkkFQuBZx8H5Vcg0CB+xUg1pn5PwnuSOJHv61nLLigZ1qqunjju4kdq0J8mKj7uVxFgtv58Qsv2inJrBVDbIk+Nx/ZDaoiGc35cbvfASG+48cT1UpMQZ/44aR1uAD7kYEp1/8gZ6OPVe+LzlRkoSpQcSbUSrw+PJm2buRtQx171N9gY4Nyd3z1oEerAqRYdV3nZB7NWV2qqFF8hu45jGfPGswjNxWWAkBC/do1JNmXciewutl6pMKhVRWl7T8eHvHal2xj1BEjYHM1bWZLE+T2dNxyQBQXrzUqGbPThvxZ/q8QUMzgk79TR98oUZ+CpA4ekgm2lvHDksGV95GoNd1Noz3h51f0hC8CLBUBOwXqpE/LtEtNAXgEIM20VbftCUcuSpHbRziPHKBgQNhkFwCvN/+sUnV+Dw4fed4AMqatlou6xtMdQQ57a3CZcKzxybkF0EfQE1+AVqRBA7Z6MsOMAhRhdB2mwCKYscSZWf9Fsyx2TNr11d022DhgVMOkdLS+XdExmNr1nRHuuebHZ9vtaSgjYi7n7ka9HDTz92sCodRmAFoDwOMgomttYLWEAtlYenEz3PyXFrTIEeFpXiLHrY49w7+kDOvVROFu0jrf0yXrw4xg+cVTIUhg7wsZaZITELvGKgdlkOagsKVmdMHiFdgsCJvX38t6JBmMkUp8YgQf5Fj5tF4Jl9MHTCDrCJG+/RQYwCsyL2qFLyVVEH9V3hk5afKqwN06s2hQzOhnD1n4xFsTKRHw1Xiv//kFDjOcG8CuUeVQbxjN93cwohIyeBcKIKC4KrvmfvxisQvgscSINfVRelgFacipwIWGxxsdLiCbOl8doQ3cYviSdoK8+9ZuElwm6Vd71KwA6ViIcbaJJ9ZLDiihYXIka1oxh1SJIgy098NRkdTivj21vs9ns0Eg9+OBOAIqVwaQLff+a4TQbI6yUfzf9/MLWQ2ri8R6MpPDW45tGIW3xORNqPubdLlfPjev24vCQm+wsIIrKF1UFLOaf4I/CYamoh4PWVYA3mvlPQxhJs8LKy4xpe9WpBz0aUNDXiA8nv22IE3vvK28O8x1/BFPQI1f/tZOkC84P15NnEmlV4ixo/18FKfVcATZI+9TQ1P5KAdfhRFcYG7WFBmVj+Qf7+xk6XcGh+Mt9GxlrU/3sE8T1cpjpqecFvL2vxrnst1OtMwoGNKoO2j2dIElskum2bAOE4+EeGl9Lwilh38ye7lhSPH68Fsn5Pay1fJEHsZ2yC1tHPICz+x3jSrvj1B5TvT1R9dnEo9m8S4ZD0q8OVYIMovlT4/stkyfl3J3JYjtm9oGtdGx9WTQbynxdqqWdbFSTszVtPb7mAcYoQaSbpp6DkdcHr7xMZEYae0VHKpKkr7Yn7hq/ep3Yc+M+rrE7x3Q8snoj2VzOlYxxo/RoMtShe+X0jdQa9QeW1oyzhxt9qoyau8q+gUs8rjAM28mPFv8jjwhmvF+wExu7bmdSG02vSfnGYrfdVXsPwYcmlei+8cPAHaiECibSdN7qIc5g5Ljg6azNFjEBcEksKnrIt6FoM1jRg37Nws1EpHKltHwvNHoxonS5sV0Abesta8LhfQ/7k6T2is65ShKvnXJWx0iVIkmfG/r+vu5FoP8PbDhXazSy1y4Q16CmaXSdpplndp2tQWnKvv58SS7HQTAH1C6GJygr1BItL8EDmkmwmejHKNNMqK3H9BlFLi9ABzYImdNAo1z1eEKzk81Qoc6QJ/fmelyhBkPtWml1ovup5PNyrdjCLScTYSTkcDtV3kQHir4icX3P+U54m+fmZd0YL/AiYK2v3Lgg6aUUn5L83CLX22YBE2mwDl98sR0WT4RsLTtNT67c3O8+wD28cKY/eNmcl91lkyb52p4cf6Qj83lqhat0MkB3rgg/rCDxU6kiVNwUkZp1nSpTCp+lLkBAAAAAFC1zXx9ptmlAAAAAAAAAAx4LTDCIKy21ilRK4frELJ+JhQsvB0IjOJc7pigQxF137icBDj7TW/kUITtnCmprHjLsFNnx1TIxXenRzCG7sFouFqsBVpjPyWyQ0a6zx8mXD8KYvQuBJnbhpsjc06mPD7lvD7yoEY0uwHFQeDtpOi/0CJFNQpsGeOacSlzfbiBRpq7ppnQ/U7DLSU4Ge7kPVzltgcaOH4lFCzVxeRyHZb8c3f/Y0uVNsRDd8EIc6NVIRWK0obo6kCxBK9JhIQMc9tZuNB7f2+LtI3bBtU19W/t7yh5LK42HLwWoGa1PaN46nWQ0Nsd5DkBvEW7bqHqQn3F0oI9xVS4xUb+0lJd919EL6GKzrzPOAuLWVULZW7Gjui/CLEadEZ8INdZoFzio6S8qv1tKhLeBFBS+2z/yiog2QVcSyE82qhMgITGYniF4Q+G8vRoXrKnYhps3LpK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1. Run the following to create the winml\_ctx\_generation.py file in the working directory.

notepad.exe winml_ctx_generation.py
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2. Copy the following code into the *winml\_ctx\_generation.py* file and save.

# File_name : winml_ctx_generation.py
        
        import onnxruntime as ort
        import numpy as np
        import os
        import time
        #Qualcomm utility for pre-/postprocessing of input/outputs in model inference
        import io_utils

        reg_winml_start_time = time.time()
        from pathlib import Path
        from importlib import metadata
        site_packages_path = Path(str(metadata.distribution('winrt-runtime').locate_file('')))
        dll_path = site_packages_path / 'winrt' / 'msvcp140.dll'
        if dll_path.exists():
           dll_path.unlink()
        
        from winui3.microsoft.windows.applicationmodel.dynamicdependency.bootstrap import (
           InitializeOptions,
           initialize
        )
        
        import winui3.microsoft.windows.ai.machinelearning as winml
        import onnxruntime as ort
        
        with initialize(options=InitializeOptions.ON_NO_MATCH_SHOW_UI):
           catalog = winml.ExecutionProviderCatalog.get_default()
           # Filter EPs that the app supports
           available_provider = catalog.find_all_providers()
           print(f"all available procider : ")
           for provider in available_provider:
              print(f"{provider.name}: {provider.ready_state}")
        
           possible_provider_list = ['QNNExecutionProvider']
           providers = [provider for provider in available_provider  if provider.name in possible_provider_list]
           print(f"Device providers : ",providers)
           for p in providers:
              print(f"providers  details  : {p.name} , {p.ready_state}")
        
           ep_to_regitsted = [provider for provider in providers if provider.ready_state in [winml.ExecutionProviderReadyState.NOT_READY,winml.ExecutionProviderReadyState.NOT_PRESENT]]
           print("ep_to_regitsted : ",ep_to_regitsted)
           # Download and install a NotPresent EP
           for provider in ep_to_regitsted:
              print(f"processing provider : {provider.name}")
              result = provider.ensure_ready_async().get()
              print("ensure_ready_async -> status:", result.status, "diag=", result.diagnostic_text)

           # Register all ready EPs ,  Add the provider to the app's dependency graph if needed
           for provider in [provider for provider in providers if provider.ready_state == winml.ExecutionProviderReadyState.READY]:
              ort.register_execution_provider_library(provider.name, provider.library_path)
              print(f"Provider is regitered : {provider.name} at path  : {provider.library_path}")
        
           reg_winml_end_time = time.time()
           reg_winml_execution_time = ((reg_winml_end_time-reg_winml_start_time))
           print("WinML Registration Execution Time: ", reg_winml_execution_time, "s")
        
        options = ort.SessionOptions()
        options.set_provider_selection_policy(ort.OrtExecutionProviderDevicePolicy.PREFER_NPU)
        
        # model path
        onnx_model_path  = "mobilenet_v2.onnx"
        compile_onnx_model_path = "mobilenet_v2_winml_qnn_ctx.onnx"
        
        # Compile the model.
        print("\nCompiling the model for offline execution : ...")
        model_compiler = ort.ModelCompiler(
           options,
           onnx_model_path,
           embed_compiled_data_into_model=True,
           external_initializers_file_path=None,
        )
        model_compiler.compile_to_file(compile_onnx_model_path)
        print(f"\nContext binary model is saved at : {compile_onnx_model_path}\n")
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python .\winml_ctx_generation.py
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    This creates **mobilenet\_v2\_winml\_qnn\_ctx.onnx** ctx onnx model that can be used for offline model inferencing.

![../../_images/WinML_CTX.png](data:image/png;base64,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)
    3. Run the following to create the winml\_AOT.py file in the working directory.

notepad.exe winml_AOT.py
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    4. Copy the following code into the *winml\_AOT.py* file and save.

# File_name : winml_AOT.py
        
        import onnxruntime as ort
        import numpy as np
        import os
        import time
        #Qualcomm utility for pre-/postprocessing of input/outputs in model inference
        import io_utils
        
        code_start_time = time.time()
        
        reg_winml_start_time = time.time()
        from pathlib import Path
        from importlib import metadata
        site_packages_path = Path(str(metadata.distribution('winrt-runtime').locate_file('')))
        dll_path = site_packages_path / 'winrt' / 'msvcp140.dll'
        if dll_path.exists():
           dll_path.unlink()
        
        from winui3.microsoft.windows.applicationmodel.dynamicdependency.bootstrap import (
           InitializeOptions,
           initialize
        )
        
        import winui3.microsoft.windows.ai.machinelearning as winml
        import onnxruntime as ort
        
        with initialize(options=InitializeOptions.ON_NO_MATCH_SHOW_UI):
           catalog = winml.ExecutionProviderCatalog.get_default()
           # Filter EPs that the app supports
           available_provider = catalog.find_all_providers()
           print(f"all available procider : ")
           for provider in available_provider:
              print(f"{provider.name}: {provider.ready_state}")
        
           possible_provider_list = ['QNNExecutionProvider']
           providers = [provider for provider in available_provider  if provider.name in possible_provider_list]
           print(f"Device providers : ",providers)
           for p in providers:
              print(f"providers  details  : {p.name} , {p.ready_state}")
        
           ep_to_regitsted = [provider for provider in providers if provider.ready_state in [winml.ExecutionProviderReadyState.NOT_READY,winml.ExecutionProviderReadyState.NOT_PRESENT]]
           print("ep_to_regitsted : ",ep_to_regitsted)
           # Download and install a NotPresent EP
           for provider in ep_to_regitsted:
              print(f"processing provider : {provider.name}")
              result = provider.ensure_ready_async().get()
              print("ensure_ready_async -> status:", result.status, "diag=", result.diagnostic_text)

           # Register all ready EPs ,  Add the provider to the app's dependency graph if needed
           for provider in [provider for provider in providers if provider.ready_state == winml.ExecutionProviderReadyState.READY]:
              ort.register_execution_provider_library(provider.name, provider.library_path)
              print(f"Provider is regitered : {provider.name} at path  : {provider.library_path}")
        
           reg_winml_end_time = time.time()
           reg_winml_execution_time = ((reg_winml_end_time-reg_winml_start_time))
           print("WinML Registration Execution Time: ", reg_winml_execution_time, "s")
        
        # Step1: Runtime and model initialization
        options = ort.SessionOptions()
        options.set_provider_selection_policy(ort.OrtExecutionProviderDevicePolicy.PREFER_NPU)
        
        # model path
        onnx_model_path = "mobilenet_v2_winml_qnn_ctx.onnx"
        
        # Prefer explicit provider selection; fall back to CPU if QNN isn’t present
        providers = [("QNNExecutionProvider", {"backend_path": "QnnHtp.dll"})]
        
        # Create inference session
        sess_start_time = time.time()
        session = ort.InferenceSession(onnx_model_path, sess_options=options)
        sess_end_time = time.time()
        sess_execution_time = ((sess_end_time-sess_start_time))

        # Step2: Input/Output handling, Generate raw input
        # github repo for below artifact: https://github.com/quic/wos-ai/tree/main/Artifacts
        img_path = "https://raw.githubusercontent.com/quic/wos-ai/refs/heads/main/Artifacts/coffee_cup.jpg"
        
        raw_img = io_utils.preprocess(img_path)
        
        # Model input and output names
        outputs = session.get_outputs()[0].name
        inputs = session.get_inputs()[0].name
        
        # Step3: Model inferencing using preprocessed input.
        iteration =100
        print("Total number of iteration : ",iteration)
        start_time = time.time()
        print_first_execution_time= True
        first_exe_execution_time=0
        execution_time =0
        for i in range(iteration):
           if print_first_execution_time:
              first_exe_start_time = time.time()
              prediction = session.run([outputs], {inputs: raw_img})
              first_exe_end_time = time.time()
              first_exe_execution_time = ((first_exe_end_time-first_exe_start_time) * 1000)
        
              print_first_execution_time=False
           else:
              start_time = time.time()
              prediction = session.run([outputs], {inputs: raw_img})
              end_time = time.time()
              execution_time = execution_time +(end_time-start_time)
        execution_time = (execution_time * 1000)/iteration
        
        # Step4: Output postprocessing
        io_utils.postprocess(prediction)
        print("\n\n********************* Summary **********************\n")
        print("Total number of iteration : ",iteration)
        print("Session Creation Time: ", sess_execution_time, "s")
        print("First Execution Time: ", first_exe_execution_time, "ms")
        print("Average Execution Time: ", execution_time, "ms")
        code_end_time = time.time()
        code_execution_time = ((code_end_time-code_start_time))
        print("Code Execution Time: ", code_execution_time, "s")
        print("\n*******************************************************")
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    This script uses Python utilities to preprocess a specified .jpg image according to the model’s requirements and sends it for inferenceing.

python .\winml_AOT.py
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    The results from the Windows ML inference are then processed to show the identified object’s class and probability, as shown in the following example.

![../../_images/WinML_AOT.png](data:image/png;base64,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)

Note

The previous tutorial demonstrates inference using an FP16 MobileNet‑V2 model. You can further optimize the model through [Olive](https://docs.qualcomm.com/doc/80-62010-1/topic/olive.html#olive-optimization) by applying quantization and perform inference on the optimized model using WindowsML Runtime.

## Accuracy debugger

QAIRT provides the `qairt-accuracy-debugger` tool for identifying inaccuracies in neural networks at the layer level. This tool performs a comparison between reference (golden) outputs generated by executing a model within an ML framework and the corresponding outputs obtained when running the same model on target hardware platforms, such as NPUs, CPUs, or GPUs.

Please refer to the [Accuracy Debugger](https://docs.qualcomm.com/doc/80-62010-1/topic/accuracy_debugger.html#accuracy-debugger) section for a sample example using the MobileNet\_v2 model.

## Performance analysis

Optrace profiling provides AI Runtime SDK users with visibility into how an AI model or network is executed on NPU hardware. It offers a detailed analysis of the execution flow and performance characteristics of the network on the NPU.
For more details, see [QAIRT Optrace](https://docs.qualcomm.com/doc/80-62010-1/topic/qnn-optrace.html#optrace).

## More details

> 
> 
> - [Microsoft WinML Nuget Releases](https://www.nuget.org/packages/Microsoft.Windows.AI.MachineLearning)
> - [How to build C++ Sample App](https://github.com/microsoft/WindowsAppSDK-Samples/tree/main/Samples/WindowsML/cpp/CppConsoleDesktop)

## FAQs

- **Which Python version is supported?**

> 
> 
> - Windows ML requires Python amd64 versions 3.10.x to 3.13.x
> 
> 
> 
> > 
> > 
> > - Tutorial examples validated with [python-3.12.6-amd64](https://www.python.org/ftp/python/3.12.6/python-3.12.6-amd64.exe)
- **How to run inference using C/C++?**

> 
> 
> - Use this  [example](https://github.com/microsoft/WindowsAppSDK-Samples/tree/main/Samples/WindowsML/cpp/CppConsoleDesktop)  to run inference using C/C++.
- **How to check for availability of runtimes or hardwares on the device?**

    - The following code demonstrates the availability of the different hardware on the device.

> 
> 
> import onnxruntime as ort
>         
>         # get the available execution provider devices details
>         ep_devices = ort.get_ep_devices()
>         for ep in ep_devices:
>            print(ep.ep_name, ep.device.type)
>         Copy to clipboard
- **How can EP be registered while session creation?**

> 
> 
> - The following code demonstrates how to pass and register the EP:
> 
> 
> def create_session(args, execution_provider_option):
>             
>                print("execution_provider_option : ",execution_provider_option)
>                ep_options = {}
>                ep_options.update(execution_provider_option)
>                all_ep_devices = ort.get_ep_devices()
>                selected_ep_devices = [ep_device for ep_device in all_ep_devices if ep_device.ep_name == 'QNNExecutionProvider']
>                print(f'WInML selected_ep_devices: {selected_ep_devices}, ep_options: {ep_options}')
>                options.add_provider_for_devices(selected_ep_devices, ep_options)
>             
>                start_time = time.time()
>                options.set_provider_selection_policy(ort.OrtExecutionProviderDevicePolicy.PREFER_NPU)
>             Copy to clipboard
- **How to convert any model into ONNX?**

    - Follow the [Convert a model with AI Toolkit for VS Code](https://code.visualstudio.com/docs/intelligentapps/modelconversion) to convert the model into ONNX format.
- **How to quantize a model and execute using Windows ML?**

    - Use [Olive](https://docs.qualcomm.com/doc/80-62010-1/topic/olive.html#olive-optimization) for model quantization, optimization, and context binary creation. The context binary model can be run using the above execution script.
- **More details about context binary feature?**

    - More details about context binary feature can be found at  [Context Binary](https://onnxruntime.ai/docs/execution-providers/QNN-ExecutionProvider.html#qnn-context-binary-cache-feature)

Last Published: Jun 16, 2026

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