class_docstr: Unit-test mock environment.
  
  Args:
      states: States specification.
      actions: Actions specification.
      min_timesteps: Minimum number of timesteps.
code: def random_state_function(cls, state_spec):
      shape = state_spec.get("shape", ())
      dtype = state_spec.get("type", "float")
  
      if dtype == "bool":
          return lambda: np.random.random_sample(size=shape) >= 0.5
  
      elif dtype == "int":
          num_values = state_spec["num_values"]
          return lambda: np.random.randint(
              low=0, high=num_values, size=shape
          )
  
      elif dtype == "float":
          if "min_value" in state_spec:
              min_value = state_spec["min_value"]
              max_value = state_spec["max_value"]
              return lambda: (
                  min_value
                  + (max_value - min_value)
                  * np.random.random_sample(size=shape)
              )
  
          else:
              return lambda: np.random.standard_normal(size=shape)
  
file_path: tensorforce/test/unittest_environment.py
imports: from collections import OrderedDict
  from random import random
  import numpy
  from tensorforce import Environment, TensorforceError, util
label_desc: Use image node_id 5 for calling the UnittestEnvironment obj's underlying member method code with example usage: obj.random_state_function(cls, state_spec) and returns: function, function, function, function