tensorflow_probability/python/distributions/transformed_distribution.py
def _pick_scalar_condition(pred, cond_true, cond_false):
  """Convenience function which chooses the condition based on the predicate."""
  # Note: This function is only valid if all of pred, cond_true, and cond_false
  # are scalars. This means its semantics are arguably more like tf.cond than
  # tf.where even though we use tf.where to implement it.
  pred_ = tf.get_static_value(tf.convert_to_tensor(value=pred))
  if pred_ is None:
    return tf.where(pred, cond_true, cond_false)
  return cond_true if pred_ else cond_false
def _pick_scalar_condition(pred, cond_true, cond_false):
  # Note: This function is only valid if all of pred, cond_true, and cond_false
  # are scalars. This means its semantics are arguably more like tf.cond than
  # tf.where even though we use tf.where to implement it.
  pred_ = tf.get_static_value(tf.convert_to_tensor(value=pred))
  if pred_ is None:
    return tf.where(pred, cond_true, cond_false)
  return cond_true if pred_ else cond_false
def _pick_scalar_condition ( pred , cond_true , cond_false ) : # Note: This function is only valid if all of pred, cond_true, and cond_false # are scalars. This means its semantics are arguably more like tf.cond than # tf.where even though we use tf.where to implement it. pred_ = tf . get_static_value ( tf . convert_to_tensor ( value = pred ) ) if pred_ is None : return tf . where ( pred , cond_true , cond_false ) return cond_true if pred_ else cond_false
Convenience function which chooses the condition based on the predicate.
Convenience function which chooses the condition based on the predicate .