multiplication algorithm using that datatype internally is
        available. Otherwise float32 matrix multiplications are
        computed as if the precision is "high".
Note:
 This does not change the output dtype of float32 matrix
 multiplications, it controls how the internal computation of the
 matrix multiplication is performed.

Note:
 This does not change the precision of convolution operations.
 Other flags, like *torch.backends.cudnn.allow_tf32*, may control
 the precision of convolution operations.

Note:
 This flag currently only affects one native device type: CUDA. If
 "high" or "medium" are set then the TensorFloat32 datatype will
 be used when computing float32 matrix multiplications, equivalent
 to setting *torch.backends.cuda.matmul.allow_tf32 = True*. When
 "highest" (the default) is set then the float32 datatype is used
 for internal computations, equivalent to setting
 *torch.backends.cuda.matmul.allow_tf32 = False*.
