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 _construct_state(self, state_tuple):
      state = utils.obj_array(self.num_factors)
  
      for f, ns in enumerate(self.num_states):
          state[f] = utils.onehot(state_tuple[f], ns)
  
      return state
  
imports: from pymdp.envs import Env
  from pymdp import utils, maths
  import numpy
label_desc: Use image node_id 10 for calling the TMazeEnvNullOutcome obj's underlying member method code with example usage: obj._construct_state(state_tuple) and returns: state