ravens/tasks/defs_cables.py
def reset(self, env, last_info=None):
        """Reset to start an episode.

        If generating training data for goal-conditioned Transporters with
        `main.py` or goal images using `generate_goals.py`, then call the
        superclass. The code already puts the bead poses inside `info`. For
        this env it's IDs 4 through 27 (for 24 beads) but I scale it based on
        num_parts in case we change this value.

        If loading using `load.py` (detect with self.goal_cond_testing) then
        must make targets based on loaded info. However, we still have to
        randomly create the cable, so the easiest way might be to make the
        cable as usual, and then just override the 'places' key later.
        """
        super().reset(env)

        # Override with targets from last_info (and the goal image!).
        if self.goal_cond_testing:
            assert last_info is not None
            self.goal['places'] = self._get_goal_info(last_info)
Reset to start an episode.

        If generating training data for goal-conditioned Transporters with
        `main.py` or goal images using `generate_goals.py`, then call the
        superclass. The code already puts the bead poses inside `info`. For
        this env it's IDs 4 through 27 (for 24 beads) but I scale it based on
        num_parts in case we change this value.

        If loading using `load.py` (detect with self.goal_cond_testing) then
        must make targets based on loaded info. However, we still have to
        randomly create the cable, so the easiest way might be to make the
        cable as usual, and then just override the 'places' key later.
        
Reset to start an episode.
If generating training data for goal-conditioned Transporters with
`main.py` or goal images using `generate_goals.py`, then call the
superclass. The code already puts the bead poses inside `info`. For
this env it's IDs 4 through 27 (for 24 beads) but I scale it based on
num_parts in case we change this value.

If loading using `load.py` (detect with self.goal_cond_testing) then
must make targets based on loaded info. However, we still have to
randomly create the cable, so the easiest way might be to make the
cable as usual, and then just override the 'places' key later.
Reset to start an episode . If generating training data for goal - conditioned Transporters with ` main . py ` or goal images using ` generate_goals . py ` then call the superclass . The code already puts the bead poses inside ` info ` . For this env it ' s IDs 4 through 27 ( for 24 beads ) but I scale it based on num_parts in case we change this value . If loading using ` load . py ` ( detect with self . goal_cond_testing ) then must make targets based on loaded info . However we still have to randomly create the cable so the easiest way might be to make the cable as usual and then just override the ' places ' key later .
def reset(self, env, last_info=None):
        super().reset(env)
        if self.goal_cond_testing:
            assert last_info is not None
            self.goal['places'] = self._get_goal_info(last_info)
def reset ( self , env , last_info = None ) : super ( ) . reset ( env ) if self . goal_cond_testing : assert last_info is not None self . goal [ 'places' ] = self . _get_goal_info ( last_info )
Reset to start an episode.
Reset to start an episode .
"""Reset to start an episode.

        If generating training data for goal-conditioned Transporters with
        `main.py` or goal images using `generate_goals.py`, then call the
        superclass. The code already puts the bead poses inside `info`. For
        this env it's IDs 4 through 27 (for 24 beads) but I scale it based on
        num_parts in case we change this value.

        If loading using `load.py` (detect with self.goal_cond_testing) then
        must make targets based on loaded info. However, we still have to
        randomly create the cable, so the easiest way might be to make the
        cable as usual, and then just override the 'places' key later.
        """ # Override with targets from last_info (and the goal image!).
self
env
last_info