class_docstr: Create a bernoulli distribution object.
  
  Parameters
  ----------
  prob : Tensor or scalar, default None
      Probability of sampling `1`.
  logit : Tensor or scalar, default None
      The log-odds of sampling `1`.
class_docstr_tok: ["Create","a","bernoulli","distribution","object",".","Parameters","--","--","--","--","--","prob",":","Tensor","or","scalar",",","default","None","Probability","of","sampling","`","1","`",".","logit",":","Tensor","or","scalar",",","default","None","The","log-odds","of","sampling","`","1","`","."]
code: def _log_normalizer(self, x):
      # pylint: disable=arguments-differ
      return np.log(1 + np.exp(x))
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/bernoulli.py
imports: from .exp_family import ExponentialFamily
  from .utils import prob2logit, logit2prob, cached_property, sample_n_shape_converter
  from .constraint import Boolean, Interval, Real
  from ....None import np, npx
label_desc: Use image node_id 11 for calling the Bernoulli obj's underlying member method code with example usage: obj._log_normalizer(x) and returns: np