def _check_input(self, shape_, rate):
shape_ = self._convert2tensor(shape_)
rate = self._convert2tensor(rate)
if shape_.shape != rate.shape:
shape = np.broadcast(shape_.value, rate.value).shape
if shape_.shape != shape:
shape_ = broadcast_to(shape_, shape)
if rate.shape != shape:
rate = broadcast_to(rate, shape)
return shape_, rate
def _check_input(self, x, y):
x = self._convert2tensor(x)
y = self._convert2tensor(y)
if x.shape != y.shape:
shape = np.broadcast(x.value, y.value).shape
if x.shape != shape:
x = broadcast_to(x, shape)
if y.shape != shape:
y = broadcast_to(y, shape)
return x, y
def _check_input(self, mean, std):
mean = self._convert2tensor(mean)
std = self._convert2tensor(std)
if mean.shape != std.shape:
shape = np.broadcast(mean.value, std.value).shape
if mean.shape != shape:
mean = broadcast_to(mean, shape)
if std.shape != shape:
std = broadcast_to(std, shape)
return mean, std