code: def create_distributions(self):
          
          distributions = dict()
          for name in sorted(self.actions_spec):
              action = self.actions_spec[name]
  
              if self.distributions_spec is not None and name in self.distributions_spec:
                  kwargs = dict(action)
                  kwargs['scope'] = name
                  kwargs['summary_labels'] = self.summary_labels
                  distributions[name] = Distribution.from_spec(
                      spec=self.distributions_spec[name],
                      kwargs=kwargs
                  )
  
              elif action['type'] == 'bool':
                  distributions[name] = Bernoulli(
                      shape=action['shape'],
                      scope=name,
                      summary_labels=self.summary_labels
                  )
  
              elif action['type'] == 'int':
                  distributions[name] = Categorical(
                      shape=action['shape'],
                      num_actions=action['num_actions'],
                      scope=name,
                      summary_labels=self.summary_labels
                  )
  
              elif action['type'] == 'float':
                  if 'min_value' in action:
                      distributions[name] = Beta(
                          shape=action['shape'],
                          min_value=action['min_value'],
                          max_value=action['max_value'],
                          scope=name,
                          summary_labels=self.summary_labels
                      )
  
                  else:
                      distributions[name] = Gaussian(
                          shape=action['shape'],
                          scope=name,
                          summary_labels=self.summary_labels
                      )
  
          return distributions
docs: Creates and returns the Distribution objects based on self.distributions_spec.
  
          Returns: Dict of distributions according to self.distributions_spec.