Unable to convert ResNet-50-Pytorch model

Unable to convert the downloaded model. Error:

(openvino_env) (base) testuser@clftower:~/workspace$ omz_downloader --name resnet-50-pytorch
################|| Downloading resnet-50-pytorch ||################

========== Downloading /home/testuser/workspace/public/resnet-50-pytorch/resnet50-19c8e357.pth
... 100%, 100100 KB, 32465 KB/s, 3 seconds passed

(openvino_env) (base) testuser@clftower:~/workspace$ omz_converter --name resnet-50-pytorch
========== Converting resnet-50-pytorch to ONNX
Conversion to ONNX command: /home/testuser/openvino_env/bin/python -- /home/testuser/openvino_env/lib/python3.12/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-name=resnet50 --weights=/home/testuser/workspace/public/resnet-50-pytorch/resnet50-19c8e357.pth --import-module=torchvision.models --input-shape=1,3,224,224 --output-file=/home/testuser/workspace/public/resnet-50-pytorch/resnet-v1-50.onnx --input-names=data --output-names=prob

ERROR: Weights from /home/testuser/workspace/public/resnet-50-pytorch/resnet50-19c8e357.pth cannot be loaded for model resnet50! Check matching between model and weights
Cannot use ``weights_only=True`` with files saved in the legacy .tar format. In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.

FAILED:
resnet-50-pytorch
(openvino_env) (base) testuser@clftower:~/workspace$ 

Steps used on Ubuntu 24.04:

sudo apt install python3.12-venv
python3 -m venv openvino_env
source openvino_env/bin/activate
python -m pip install --upgrade pip
python -m pip install openvino
python -c "from openvino import Core; print(Core().available_devices)"      #  ---- Please verify that both CPU and GPU are listed -----
pip install openvino-dev
pip install onnx
pip install torch torchvision

mkdir workspace
cd workspace

omz_downloader --name resnet-50-pytorch
omz_converter --name resnet-50-pytorch

Start by reproducing the failure with omz_downloader and omz_converter for resnet-50-pytorch, then inspect the generated command and pytorch_to_onnx.py. Compare the reported torch.load compatibility error with the model conversion path. Done means the downloaded ResNet-50-PyTorch model converts successfully without the shown error.