code: def n_arms(N=2):
      """
      Each arm yields a reward from a different Gaussian.
  
      The correct arm is arm 0.
  
      """
      rng = np.random.default_rng(123)
      x = hp.choice("x", [0, 1])
      reward_mus = as_apply([-1] + [0] * (N - 1))
      reward_sigmas = as_apply([1] * N)
      return {
          "loss": scope.normal(
              reward_mus[x], reward_sigmas[x], rng=rng
          ),
          "loss_variance": 1.0,
          "status": base.STATUS_OK,
      }
  
imports: import unittest
  import numpy
  from hyperopt import Trials, Domain, fmin, hp, base
  from hyperopt.rand import suggest
  from hyperopt.pyll import as_apply
  from hyperopt.pyll import scope
label_desc: Use image node_id 6 for calling a global function with example usage: n_arms(N) and returns: dict