nncf/experimental/torch/nas/bootstrapNAS/elasticity/base_handler.py
def activate_supernet(self) -> None:
        """
        Activates the Supernet - the original network to which elasticity was applied.
        The Supernet is usually a maximum Subnet. But it might be not the case if maximum value of elastic properties
        is limited for efficiency of search. For instance, a supernet may contain 32 convolutional channels,
        but maximum subnet can be restricted to have 24 channels, at maximum.
        """

        Activates the Supernet - the original network to which elasticity was applied.
        The Supernet is usually a maximum Subnet. But it might be not the case if maximum value of elastic properties
        is limited for efficiency of search. For instance, a supernet may contain 32 convolutional channels,
        but maximum subnet can be restricted to have 24 channels, at maximum.
        
Activates the Supernet - the original network to which elasticity was applied.
The Supernet is usually a maximum Subnet. But it might be not the case if maximum value of elastic properties
is limited for efficiency of search. For instance, a supernet may contain 32 convolutional channels,
but maximum subnet can be restricted to have 24 channels, at maximum.
Activates the Supernet - the original network to which elasticity was applied . The Supernet is usually a maximum Subnet . But it might be not the case if maximum value of elastic properties is limited for efficiency of search . For instance a supernet may contain 32 convolutional channels but maximum subnet can be restricted to have 24 channels at maximum .
def activate_supernet(self) -> None:
def activate_supernet ( self ) -> None :
Activates the Supernet - the original network to which elasticity was applied.
Activates the Supernet - the original network to which elasticity was applied .
"""
        Activates the Supernet - the original network to which elasticity was applied.
        The Supernet is usually a maximum Subnet. But it might be not the case if maximum value of elastic properties
        is limited for efficiency of search. For instance, a supernet may contain 32 convolutional channels,
        but maximum subnet can be restricted to have 24 channels, at maximum.
        """
self