This code segment functions to: Dynamically override tensor access operations to enable on-demand device transfer of neural network parameters and buffers while preserving original module functionality.

```python
[TASK_BEGIN]
class DynamicSwapInstaller:
    @staticmethod
    def _install_module(module: torch.nn.Module):
        if hasattr(module, '_original_class'):
            return

        original_class = module.__class__

        class HackedModule(original_class):
            def __getattr__(self, name):
                if name in self._modules:
                    submodule = self._modules[name]
                    if submodule.device == cpu:
                        move_model_to_device_with_memory_preservation(submodule, gpu)
                return super().__getattr__(name)

        HackedModule.__name__ = original_class.__name__
        module._original_class = original_class
        module.__class__ = HackedModule

    @staticmethod
    def _uninstall_module(module: torch.nn.Module):
        if hasattr(module, '_original_class'):
            module.__class__ = module._original_class
            del module._original_class

    @staticmethod
    def install_model(model: torch.nn.Module):
        for module in model.modules():
            DynamicSwapInstaller._install_module(module)

    @staticmethod
    def uninstall_model(model: torch.nn.Module):
        for module in model.modules():
            DynamicSwapInstaller._uninstall_module(module)
[TASK_END]
```