tensorflow_probability/python/distributions/batch_reshape.py
def _sample_shape(self, x):
    """Computes graph and static `sample_shape`."""
    x_ndims = (
        tf.rank(x) if tensorshape_util.rank(x.shape) is None else
        tensorshape_util.rank(x.shape))
    event_ndims = (
        tf.size(input=self.event_shape_tensor())
        if tensorshape_util.rank(self.event_shape) is None else
        tensorshape_util.rank(self.event_shape))
    batch_ndims = (
        tf.size(input=self._batch_shape_unexpanded)
        if tensorshape_util.rank(self.batch_shape) is None else
        tensorshape_util.rank(self.batch_shape))
    sample_ndims = x_ndims - batch_ndims - event_ndims
    if isinstance(sample_ndims, int):
      static_sample_shape = x.shape[:sample_ndims]
    else:
      static_sample_shape = tf.TensorShape(None)
    if tensorshape_util.is_fully_defined(static_sample_shape):
      sample_shape = np.int32(static_sample_shape)
    else:
      sample_shape = tf.shape(input=x)[:sample_ndims]
    return sample_shape, static_sample_shape
def _sample_shape(self, x):
    """Computes graph and static `sample_shape`."""
    x_ndims = (
        tf.rank(x) if tensorshape_util.rank(x.shape) is None else
        tensorshape_util.rank(x.shape))
    event_ndims = (
        tf.size(input=self.event_shape_tensor())
        if tensorshape_util.rank(self.event_shape) is None else
        tensorshape_util.rank(self.event_shape))
    batch_ndims = (
        tf.size(input=self._batch_shape_unexpanded)
        if tensorshape_util.rank(self.batch_shape) is None else
        tensorshape_util.rank(self.batch_shape))
    sample_ndims = x_ndims - batch_ndims - event_ndims
    if isinstance(sample_ndims, int):
      static_sample_shape = x.shape[:sample_ndims]
    else:
      static_sample_shape = tf.TensorShape(None)
    if tensorshape_util.is_fully_defined(static_sample_shape):
      sample_shape = np.int32(static_sample_shape)
    else:
      sample_shape = tf.shape(input=x)[:sample_ndims]
    return sample_shape, static_sample_shape
def _sample_shape ( self , x ) : x_ndims = ( tf . rank ( x ) if tensorshape_util . rank ( x . shape ) is None else tensorshape_util . rank ( x . shape ) ) event_ndims = ( tf . size ( input = self . event_shape_tensor ( ) ) if tensorshape_util . rank ( self . event_shape ) is None else tensorshape_util . rank ( self . event_shape ) ) batch_ndims = ( tf . size ( input = self . _batch_shape_unexpanded ) if tensorshape_util . rank ( self . batch_shape ) is None else tensorshape_util . rank ( self . batch_shape ) ) sample_ndims = x_ndims - batch_ndims - event_ndims if isinstance ( sample_ndims , int ) : static_sample_shape = x . shape [ : sample_ndims ] else : static_sample_shape = tf . TensorShape ( None ) if tensorshape_util . is_fully_defined ( static_sample_shape ) : sample_shape = np . int32 ( static_sample_shape ) else : sample_shape = tf . shape ( input = x ) [ : sample_ndims ] return sample_shape , static_sample_shape
Computes graph and static `sample_shape`.
Computes graph and static sample_shape .