class_docstr: Implementation of the 3-arm T-Maze environment where there is an additional null outcome within the cue modality, so that the agent
  doesn't get a random cue observation, but a null one, when it visits non-cue locations
class_docstr_tok: ["Implementation","of","the","3-arm","T-Maze","environment","where","there","is","an","additional","null","outcome","within","the","cue","modality",",","so","that","the","agent","does","n't","get","a","random","cue","observation",",","but","a","null","one",",","when","it","visits","non-cue","locations"]
code: def _get_observation(self):
      prob_obs = [
          maths.spm_dot(A_m, self._state)
          for A_m in self._likelihood_dist
      ]
  
      obs = [utils.sample(po_i) for po_i in prob_obs]
      return obs
  
imports: from pymdp.envs import Env
  from pymdp import utils, maths
  import numpy
label_desc: Use image node_id 7 for calling the TMazeEnvNullOutcome obj's underlying member method code with example usage: obj._get_observation() and returns: obs