code: def to_env_vars(self):
          
  
          env = {
              'hosts': self.hosts, 'network_interface_name': self.network_interface_name,
              'hps': self.hyperparameters, 'user_entry_point': self.user_entry_point,
              'framework_params': self.additional_framework_parameters,
              'resource_config': self.resource_config, 'input_data_config': self.input_data_config,
              'output_data_dir': self.output_data_dir,
              'channels': sorted(self.channel_input_dirs.keys()),
              'current_host': self.current_host, 'module_name': self.module_name,
              'log_level': self.log_level,
              'framework_module': self.framework_module, 'input_dir': self.input_dir,
              'input_config_dir': self.input_config_dir, 'output_dir': self.output_dir,
              'num_cpus': self.num_cpus,
              'num_gpus': self.num_gpus, 'model_dir': self.model_dir, 'module_dir': self.module_dir,
              'training_env': dict(self), 'user_args': self.to_cmd_args(),
              'output_intermediate_dir': self.output_intermediate_dir
          }
  
          for name, path in self.channel_input_dirs.items():
              env['channel_%s' % name] = path
  
          for key, value in self.hyperparameters.items():
              env['hp_%s' % key] = value
  
          return _mapping.to_env_vars(env)
docs: Environment variable representation of the training environment
  
          Returns:
              dict: an instance of dictionary