and execution duration while executing the kernel. The verbosing
   functionality can be invoked via an environment variable named
   DNNL_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.
  import torch
  model(data)
  with torch.backends.mkldnn.verbose(torch.backends.mkldnn.VERBOSE_ON):
      model(data)

Parameters:
      level -- Verbose level - "VERBOSE_OFF": Disable verbosing -
      "VERBOSE_ON":  Enable verbosing - "VERBOSE_ON_CREATION": Enable
      verbosing, including oneDNN kernel creation
torch.backends.openmp
torch.backends.openmp.is_available()