class_docstr: Bernoulli distribution.
  
  The Bernoulli distribution with `probs` parameter, i.e., the probability of a
  `1` outcome (vs a `0` outcome).
class_docstr_tok: ["Bernoulli","distribution",".","The","Bernoulli","distribution","with","`","probs","`","parameter",",","i.e.",",","the","probability","of","a","`","1","`","outcome","(","vs","a","`","0","`","outcome",")","."]
docstr: Logits computed from non-`None` input arg (`probs` or `logits`).
docstr_tok: ["Logits","computed","from","non-","`","None","`","input","arg","(","`","probs","`","or","`","logits","`",")","."]
code: def logits_parameter(self, name=None):
      """Logits computed from non-`None` input arg (`probs` or `logits`)."""
      with self._name_and_control_scope(name or "logits_parameter"):
          return self._logits_parameter_no_checks()
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensor_util
label_desc: Use image node_id 15 for calling the Bernoulli obj's underlying member method code with example usage: obj.logits_parameter(name) and returns: self