tensorflow_probability/python/stats/sample_stats.py
def _make_positive_axis(axis, ndims):
  """Rectify possibly negatively axis. Prefer return Python list."""
  axis = _make_list_or_1d_tensor(axis)

  ndims = tf.convert_to_tensor(value=ndims, name='ndims', dtype=tf.int32)
  ndims_ = tf.get_static_value(ndims)

  if _is_list_like(axis) and ndims_ is not None:
    # Static case
    positive_axis = []
    for a in axis:
      if a < 0:
        a = ndims_ + a
      positive_axis.append(a)
  else:
    # Dynamic case
    axis = tf.convert_to_tensor(value=axis, name='axis', dtype=tf.int32)
    positive_axis = tf.where(axis >= 0, axis, axis + ndims)

  return positive_axis
def _make_positive_axis(axis, ndims):
  axis = _make_list_or_1d_tensor(axis)

  ndims = tf.convert_to_tensor(value=ndims, name='ndims', dtype=tf.int32)
  ndims_ = tf.get_static_value(ndims)

  if _is_list_like(axis) and ndims_ is not None:
    # Static case
    positive_axis = []
    for a in axis:
      if a < 0:
        a = ndims_ + a
      positive_axis.append(a)
  else:
    # Dynamic case
    axis = tf.convert_to_tensor(value=axis, name='axis', dtype=tf.int32)
    positive_axis = tf.where(axis >= 0, axis, axis + ndims)

  return positive_axis
def _make_positive_axis ( axis , ndims ) : axis = _make_list_or_1d_tensor ( axis ) ndims = tf . convert_to_tensor ( value = ndims , name = 'ndims' , dtype = tf . int32 ) ndims_ = tf . get_static_value ( ndims ) if _is_list_like ( axis ) and ndims_ is not None : # Static case positive_axis = [ ] for a in axis : if a < 0 : a = ndims_ + a positive_axis . append ( a ) else : # Dynamic case axis = tf . convert_to_tensor ( value = axis , name = 'axis' , dtype = tf . int32 ) positive_axis = tf . where ( axis >= 0 , axis , axis + ndims ) return positive_axis
Rectify possibly negatively axis. Prefer return Python list.
Rectify possibly negatively axis . Prefer return Python list .