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# Source code for torch.backends.mps

    from functools import lru_cache as _lru_cache

    import torch
    from ...library import Library as _Library

    __all__ = ["is_built", "is_available", "is_macos13_or_newer"]

    def is_built() -> bool:
        r"""Returns whether PyTorch is built with MPS support. Note that this
        doesn't necessarily mean MPS is available; just that if this PyTorch
        binary were run a machine with working MPS drivers and devices, we
        would be able to use it."""
        return torch._C._has_mps

    @_lru_cache
    def is_available() -> bool:
        r"""Returns a bool indicating if MPS is currently available."""
        return torch._C._mps_is_available()

    @_lru_cache
    def is_macos13_or_newer(minor: int = 0) -> bool:
        r"""Returns a bool indicating whether MPS is running on MacOS 13 or newer."""
        return torch._C._mps_is_on_macos_13_or_newer(minor)

    _lib = None

    def _init():
        r"""Register prims as implementation of var_mean and group_norm"""
        global _lib
        if is_built() is False or _lib is not None:
            return
        from ..._decomp.decompositions import (
            native_group_norm_backward as _native_group_norm_backward,
        )
        from ..._refs import native_group_norm as _native_group_norm, var_mean as _var_mean

        _lib = _Library("aten", "IMPL")
        _lib.impl("var_mean.correction", _var_mean, "MPS")
        _lib.impl("native_group_norm", _native_group_norm, "MPS")
        _lib.impl("native_group_norm_backward", _native_group_norm_backward, "MPS")

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