def _batch_shape_tensor(self, logits_or_probs=None, total_count=None):
if logits_or_probs is None:
logits_or_probs = self._logits if self._probs is None else self._logits
total_count = self._total_count if total_count is None else total_count
return prefer_static.broadcast_shape(
prefer_static.shape(logits_or_probs),
prefer_static.shape(total_count))
def _batch_shape_tensor(self, temperature=None, logits=None):
param = logits
if param is None:
param = self._logits if self._logits is not None else self._probs
if temperature is None:
temperature = self.temperature
return prefer_static.broadcast_shape(
prefer_static.shape(temperature), prefer_static.shape(param)[:-1])
def _batch_shape_tensor(self, concentration=None, rate=None):
return prefer_static.broadcast_shape(
prefer_static.shape(self.concentration
if concentration is None else concentration),
prefer_static.shape(self.rate if rate is None else rate))
def _batch_shape_tensor(self, df=None, loc=None, scale=None):
return prefer_static.broadcast_shape(
prefer_static.shape(self.df if df is None else df),
prefer_static.broadcast_shape(
prefer_static.shape(self.loc if loc is None else loc),
prefer_static.shape(self.scale if scale is None else scale)))
def _batch_shape_tensor(self, concentration1=None, concentration0=None):
return prefer_static.broadcast_shape(
prefer_static.shape(self.concentration1
if concentration1 is None else concentration1),
prefer_static.shape(self.concentration0
if concentration0 is None else concentration0))