parameters: (initial_dist_t, target_dist_t)
docstring: Returns the static probability of sampling from the original.
  
    `tensor_util.constant_value(prob_of_original)` returns `None` if it encounters
    an Op that it isn't defined for. We have some custom logic to avoid this.
  
    Args:
      initial_dist_t: A tensor of the initial distribution.
      target_dist_t: A tensor of the target distribution.
  
    Returns:
      The probability of sampling from the original distribution as a constant,
      if it is a constant, or `None`.
docstring_summary: Returns the static probability of sampling from the original.
docstring_tokens: Returns the static probability of sampling from the original .
function: def _get_prob_original_static(initial_dist_t, target_dist_t):
    """Returns the static probability of sampling from the original.
  
    `tensor_util.constant_value(prob_of_original)` returns `None` if it encounters
    an Op that it isn't defined for. We have some custom logic to avoid this.
  
    Args:
      initial_dist_t: A tensor of the initial distribution.
      target_dist_t: A tensor of the target distribution.
  
    Returns:
      The probability of sampling from the original distribution as a constant,
      if it is a constant, or `None`.
    """
    init_static = tensor_util.constant_value(initial_dist_t)
    target_static = tensor_util.constant_value(target_dist_t)
  
    if init_static is None or target_static is None:
      return None
    else:
      return np.min(target_static / init_static)
function_tokens: def _get_prob_original_static ( initial_dist_t , target_dist_t ) : init_static = tensor_util . constant_value ( initial_dist_t ) target_static = tensor_util . constant_value ( target_dist_t ) if init_static is None or target_static is None : return None else : return np . min ( target_static / init_static )