class_docstr: States: {0, 1, ..., 9, 10}
  Actions: {-1, 0, 1}
  Action masking: action = -1 invalid for state = 0, action = 1 invalid for state = 10
  Reward:
      - Positive: [state < 5, action = 1] or [state > 5, action = -1]
      - Negative: [state < 5, action = -1] or [state > 5, action = 1]
code: def states(self):
      # States specification does not need to include action mask item
      return dict(type=int, shape=(), num_values=11)
  
file_path: tensorforce/examples/action_masking.py
imports: import numpy
  from tensorforce import Environment, Runner
label_desc: Use image node_id 2 for calling the EnvironmentWithMasking obj's underlying member method code with example usage: obj.states() and returns: dict