def METHOD_NAME(self):
    """
    Validate repvgg models (A1) as backbone.
    """
    image_size = 224
    in_channels = 3
    test_input = torch.rand((1, in_channels, image_size, image_size))
    backbone_model = RepVggA1(self.backbone_arch_params)
    model = BackboneBasedModel(backbone_model, backbone_output_channel=1280, num_classes=self.backbone_arch_params.num_classes)
    backbone_model.eval()
    model.eval()
    backbone_training_mode_output = backbone_model(test_input)
    model_training_mode_output = model(test_input)
    # check that the linear head was dropped
    self.assertFalse(backbone_training_mode_output.shape[1] == self.backbone_arch_params.num_classes)
    training_mode_sd = model.state_dict()
    for module in training_mode_sd:
        self.assertFalse("reparam" in module)  # deployment block included in training mode
    model.prep_model_for_conversion()
    deployment_mode_sd_list = list(model.state_dict().keys())
    self.assertTrue("bn.running_mean" in deployment_mode_sd_list)  # Verify non backbone batch norm wasn't fused
    for module in deployment_mode_sd_list:
        self.assertFalse("running_mean" in module and module.startswith("backbone"))  # BN were not fused
        self.assertFalse("branch" in module and module.startswith("backbone"))  # branches were not joined
    model_deployment_mode_output = model(test_input)
    self.assertFalse(False in torch.isclose(model_deployment_mode_output, model_training_mode_output, atol=1e-5))