def METHOD_NAME(self) -> None:
    timm_model = timm.create_model(self.model_id, num_classes=1000, in_chans=3, pretrained=True, checkpoint_path="")
    timm_model = replace_timm_custom_modules_with_torch_native(timm_model)
    self.model_cfg = timm_model.default_cfg
    input_size = [1] + list(timm_model.default_cfg["input_size"])
    self.dummy_tensor = torch.rand(input_size)
    if self.backend in PT_BACKENDS:
        self.model = timm_model
    if self.backend == BackendType.ONNX:
        onnx_path = self.output_model_dir / "model_fp32.onnx"
        export_to_onnx(timm_model, onnx_path, self.dummy_tensor)
        self.model = onnx.load(onnx_path)
        self.input_name = self.model.graph.input[0].name
    if self.backend in OV_BACKENDS:
        onnx_path = self.output_model_dir / "model_fp32.onnx"
        export_to_onnx(timm_model, onnx_path, self.dummy_tensor)
        export_to_ir(onnx_path, self.output_model_dir, model_name="model_fp32")
        ir_path = self.output_model_dir / "model_fp32.xml"
        core = ov.Core()
        self.model = core.read_model(ir_path)
        self.input_name = list(inp.get_any_name() for inp in self.model.inputs)[0]