nan value for the output of the context encoder

[Yuxuan314]

Traceback (most recent call last):
File "/home/uceeuni/python_projects/MetaCURE-Public/launch_experiment_metacure.py", line 254, in
main()
File "/scratch/uceeuni/anaconda3/envs/mujoco39/lib/python3.9/site-packages/click/core.py", line 764, in call
return self.main(*args, **kwargs)
File "/scratch/uceeuni/anaconda3/envs/mujoco39/lib/python3.9/site-packages/click/core.py", line 717, in main
rv = self.invoke(ctx)
File "/scratch/uceeuni/anaconda3/envs/mujoco39/lib/python3.9/site-packages/click/core.py", line 956, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/scratch/uceeuni/anaconda3/envs/mujoco39/lib/python3.9/site-packages/click/core.py", line 555, in invoke
return callback(*args, **kwargs)
File "/home/uceeuni/python_projects/MetaCURE-Public/launch_experiment_metacure.py", line 251, in main
experiment(variant)
File "/home/uceeuni/python_projects/MetaCURE-Public/launch_experiment_metacure.py", line 213, in experiment
algorithm.train()
File "/home/uceeuni/python_projects/MetaCURE-Public/rlkit/core/rl_algorithm.py", line 823, in train
self.do_training(indices,it)
File "/home/uceeuni/python_projects/MetaCURE-Public/rlkit/torch/sac/sac.py", line 594, in _do_training
self._take_step_exp(indices, context_unbatched, context_pred, context, context_rnn)
File "/home/uceeuni/python_projects/MetaCURE-Public/rlkit/torch/sac/sac.py", line 650, in _take_step_exp
z_dis = torch.distributions.Normal(z_mean, torch.sqrt(z_var))
File "/scratch/uceeuni/anaconda3/envs/mujoco39/lib/python3.9/site-packages/torch/distributions/normal.py", line 56, in init
super().init(batch_shape, validate_args=validate_args)
File "/scratch/uceeuni/anaconda3/envs/mujoco39/lib/python3.9/site-packages/torch/distributions/distribution.py", line 68, in init
raise ValueError(
ValueError: Expected parameter loc (Tensor of shape (1024, 2)) of distribution Normal(loc: torch.Size([1024, 2]), scale: torch.Size([1024, 2])) to satisfy the constraint Real(), but found invalid values:
tensor([[nan, nan],
[nan, nan],
[nan, nan],
...,
[nan, nan],
[nan, nan],
[nan, nan]], device='cuda:0', grad_fn=)