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# Source code for torch.backends.mkl

    import torch

    def is_available():
        r"""Returns whether PyTorch is built with MKL support."""
        return torch._C.has_mkl

    VERBOSE_OFF = 0
    VERBOSE_ON = 1

    class verbose:
        """
        On-demand oneMKL verbosing functionality
        To make it easier to debug performance issues, oneMKL can dump verbose
        messages containing execution information like duration while executing
        the kernel. The verbosing functionality can be invoked via an environment
        variable named `MKL_VERBOSE`. However, this methodology dumps messages in
        all steps. Those are a large amount of verbose messages. Moreover, for
        investigating the performance issues, generally taking verbose messages
        for one single iteration is enough. This on-demand verbosing functionality
        makes it possible to control scope for verbose message dumping. In the
        following example, verbose messages will be dumped out for the second
        inference only.

        .. highlight:: python
        .. code-block:: python

            import torch
            model(data)
            with torch.backends.mkl.verbose(torch.backends.mkl.VERBOSE_ON):
                model(data)

        Args:
            level: Verbose level
                - ``VERBOSE_OFF``: Disable verbosing
                - ``VERBOSE_ON``:  Enable verbosing
        """

        def __init__(self, enable):
            self.enable = enable

        def __enter__(self):
            if self.enable == VERBOSE_OFF:
                return
            st = torch._C._verbose.mkl_set_verbose(self.enable)
            assert (
                st
            ), "Failed to set MKL into verbose mode. Please consider to disable this verbose scope."
            return self

        def __exit__(self, exc_type, exc_val, exc_tb):
            torch._C._verbose.mkl_set_verbose(VERBOSE_OFF)
            return False

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