sh.SSKrSrSrSr"SS5rg)Nc6[RR$)z1Return whether PyTorch is built with MKL support.)torch_Chas_mklo/Users/tiagomarins/Projetos/claudeai/copy_bank/venv/lib/python3.13/site-packages/torch/backends/mkl/__init__.py is_availabler s 88  rc*\rSrSrSrSrSrSrSrg)verbosea On-demand oneMKL verbosing functionality. To make it easier to debug performance issues, oneMKL can dump verbose messages containing execution information like duration while executing the kernel. The verbosing functionality can be invoked via an environment variable named `MKL_VERBOSE`. However, this methodology dumps messages in all steps. Those are a large amount of verbose messages. Moreover, for investigating the performance issues, generally taking verbose messages for one single iteration is enough. This on-demand verbosing functionality makes it possible to control scope for verbose message dumping. In the following example, verbose messages will be dumped out for the second inference only. .. highlight:: python .. code-block:: python import torch model(data) with torch.backends.mkl.verbose(torch.backends.mkl.VERBOSE_ON): model(data) Args: level: Verbose level - ``VERBOSE_OFF``: Disable verbosing - ``VERBOSE_ON``: Enable verbosing cXlg)Nenable)selfrs r __init__verbose.__init__+s rcUR[:Xag[RRR UR5nU(dS5eU$)NzSFailed to set MKL into verbose mode. Please consider to disable this verbose scope.)r VERBOSE_OFFrr_verbosemkl_set_verbose)rsts r __enter__verbose.__enter__.sI ;;+ %  XX   . .t{{ ;  a ` a  rc^[RRR[5 g)NF)rrrrr)rexc_typeexc_valexc_tbs r __exit__verbose.__exit__7s ))+6rrN) __name__ __module__ __qualname____firstlineno____doc__rrr __static_attributes__rrr r r s8rr )rr r VERBOSE_ONr rrr r)s#    ++r