class_docstr: The log-normal distribution.
class_docstr_tok: ["The","log-normal","distribution","."]
code: def _sample_control_dependencies(self, x):
      assertions = []
      if not self.validate_args:
          return assertions
      assertions.append(
          assert_util.assert_non_negative(
              x, message="Sample must be non-negative."
          )
      )
      return assertions
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.util.deferred_tensor import DeferredTensor
label_desc: Use image node_id 11 for calling the LogNormal obj's underlying member method code with example usage: obj._sample_control_dependencies(x) and returns: assertions, assertions