[BugFix] patch rand_action in TransformedEnv to read the base_env method

[vmoens]

Stack from ghstack (oldest at bottom):

• [Feature,Refactor] Chess improvements: fen, pgn, pixels, san #2702
• [Feature] UnaryTransform for input entries #2700
• -> [BugFix] patch rand_action in TransformedEnv to read the base_env method #2699
• [Feature] example_data for NonTensor spec #2698

[pytorch-bot]

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[vmoens]

@kurtamohler I'm not super happy with this, as the comment says it's far from accounting for transforms that do some kind of inverse mapping but the point is that without that, a transformed chess env cannot generate random actions (because the rand_action is not the same as the one in EnvBase that would otherwise be used by TransformedEnv).

[kurtamohler]

Is there a reason we couldn't use self.action_spec.rand()?

>>> import torchrl
>>> env = torchrl.envs.PendulumEnv().append_transform(torchrl.envs.ActionDiscretizer(num_intervals=4))
>>> env.action_spec.rand()
tensor([3])
>>> env.action_spec.rand().dtype
torch.int64