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",")","."]
code: def _sample_control_dependencies(self, x):
      assertions = []
      if not self.validate_args:
          return assertions
      assertions.extend(
          distribution_util.assert_nonnegative_integer_form(x)
      )
      assertions.append(
          assert_util.assert_less_equal(
              x,
              tf.ones([], dtype=x.dtype),
              message="Sample must be less than or equal to `1`.",
          )
      )
      return assertions
  
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 22 for calling the Bernoulli obj's underlying member method code with example usage: obj._sample_control_dependencies(x) and returns: assertions, assertions