nncf/experimental/torch/nas/bootstrapNAS/elasticity/elastic_width.py
def width_list(self) -> List[int]:
        """
        list of all available widths to select from. Each value corresponds to a single element in the search space of
        operation. The search space of the model is cartesian product of search spaces of operation.
        If all widths starting from 1 to maximum number of channels with step size 1 are available, the search space
        would be prohibitively large to efficiently train and search.

        That's why there are elastic width parameters that constraint number of all available widths.

        :return: list of widths
        """
        return self._width_list

        list of all available widths to select from. Each value corresponds to a single element in the search space of
        operation. The search space of the model is cartesian product of search spaces of operation.
        If all widths starting from 1 to maximum number of channels with step size 1 are available, the search space
        would be prohibitively large to efficiently train and search.

        That's why there are elastic width parameters that constraint number of all available widths.

        :return: list of widths
        
list of all available widths to select from. Each value corresponds to a single element in the search space of
operation. The search space of the model is cartesian product of search spaces of operation.
If all widths starting from 1 to maximum number of channels with step size 1 are available, the search space
would be prohibitively large to efficiently train and search.

That's why there are elastic width parameters that constraint number of all available widths.
list of all available widths to select from . Each value corresponds to a single element in the search space of operation . The search space of the model is cartesian product of search spaces of operation . If all widths starting from 1 to maximum number of channels with step size 1 are available the search space would be prohibitively large to efficiently train and search . That ' s why there are elastic width parameters that constraint number of all available widths .
def width_list(self) -> List[int]:
        return self._width_list
def width_list ( self ) -> List [ int ] : return self . _width_list
list of all available widths to select from.
list of all available widths to select from .
"""
        list of all available widths to select from. Each value corresponds to a single element in the search space of
        operation. The search space of the model is cartesian product of search spaces of operation.
        If all widths starting from 1 to maximum number of channels with step size 1 are available, the search space
        would be prohibitively large to efficiently train and search.

        That's why there are elastic width parameters that constraint number of all available widths.

        :return: list of widths
        """
list of widths
list of widths
self