Why does my Bernoulli Distribution p out of [0,1] when using constraints.interval(.0,1.) with pyro.param()?

I met an exception:
RuntimeError: Expected p_in >= 0 && p_in <= 1 to be true, but got false.
The exception occurs in:

~/anaconda3/lib/python3.6/site-packages/torch/distributions/bernoulli.py in sample(self, sample_shape)
     88         with torch.no_grad():
---> 89             return torch.bernoulli(self.probs.expand(shape))

I am using the SVI to inference the probability of Bernoulli. But I have used constraints.interval(0,1) with the pyro.param(). Why is it still out of the range?

My model:

def model_fn(observations):
    def model_pcs(probs):
        # probs is a sequence of probabilities. E.g. [0.6, 0.1, 0.1, 0.1, 0.1]
        obs = torch.from_numpy(observations)
        abnormal_pcs = []
        for i in range(len(probs)):
            # 0 means not abnormal, 1 means abnormal
         # E,g, abnormal_pcs[0,1,0,0,1] means pc_0, pc_2, pc_3 is normal but pc_1, pc_4 is abnormal
        normal_probs = torch.from_numpy(probs*(1.0-np.array(abnormal_pcs)))
        abnormal_probs = torch.from_numpy(probs*np.array(abnormal_pcs)) 
        a = 0.5
        is_abnormal = pyro.sample("is_abnormal",dist.Bernoulli(a))
        # 50% to be normal, sample a pc with categorical distribution from normal pcs 
        # 50% to be abnormal, sample a pc with categorical distribution from abnormal pcs
        for i in range(len(observations)):
            if is_abnormal!=1.0:
                # normal
                pc = pyro.sample('pc_{}'.format(i),dist.Categorical(normal_probs),obs=obs[i])
            # abnormal
                pc = pyro.sample('pc_{}'.format(i), dist.Categorical(abnormal_probs),obs=obs[i])
    return model_pcs

The guide function is:

def param_guide(probs):
    inferenced_abnormal_pc = []
    for i in range(len(probs)):
    abnormal_pcs = []
    for i in range(len(probs)):
         # 0 means not abnormal, 1 means abnormal
    normal_probs = torch.from_numpy(probs*(1.0-np.array(abnormal_pcs)))
    abnormal_probs = torch.from_numpy(probs*np.array(abnormal_pcs))
    anomaly_score = pyro.param("anomaly_score",torch.tensor(.5),constraint=constraints.interval(.0,1.))
    is_abnormal = pyro.sample("is_abnormal",dist.Bernoulli(anomaly_score))

Can you print the value of p? This error might be triggered due to p = NAN, which might be caused by any of a number of other problems, e.g. learning rate too high.