[Typing][B-03] Add type annotations for python/paddle/distribution/beta.py

python/paddle/distribution/beta.py

@@ -11,11 +11,17 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from __future__ import annotations
+
 import numbers
+from typing import TYPE_CHECKING
 
 import paddle
 from paddle.distribution import dirichlet, exponential_family
 
+if TYPE_CHECKING:
+    from paddle import Tensor
+
 
 class Beta(exponential_family.ExponentialFamily):
     r"""
@@ -85,8 +91,10 @@ class Beta(exponential_family.ExponentialFamily):
             Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
             [-1.91923141, -0.38095081])
     """
+    alpha: Tensor
+    beta: Tensor
 
-    def __init__(self, alpha, beta):
+    def __init__(self, alpha: float | Tensor, beta: float | Tensor) -> None:
         if isinstance(alpha, numbers.Real):
             alpha = paddle.full(shape=[], fill_value=alpha)
 
@@ -102,17 +110,17 @@ def __init__(self, alpha, beta):
         super().__init__(self._dirichlet._batch_shape)
 
     @property
-    def mean(self):
+    def mean(self) -> Tensor:
         """Mean of beta distribution."""
         return self.alpha / (self.alpha + self.beta)
 
     @property
-    def variance(self):
+    def variance(self) -> Tensor:
         """Variance of beat distribution"""
         sum = self.alpha + self.beta
         return self.alpha * self.beta / (sum.pow(2) * (sum + 1))
 
-    def prob(self, value):
+    def prob(self, value: Tensor) -> Tensor:
         """Probability density function evaluated at value
 
         Args:
@@ -123,7 +131,7 @@ def prob(self, value):
         """
         return paddle.exp(self.log_prob(value))
 
-    def log_prob(self, value):
+    def log_prob(self, value: Tensor) -> Tensor:
         """Log probability density function evaluated at value
 
         Args:
@@ -146,7 +154,7 @@ def sample(self, shape=()):
         shape = shape if isinstance(shape, tuple) else tuple(shape)
         return paddle.squeeze(self._dirichlet.sample(shape)[..., 0], axis=-1)
 
-    def entropy(self):
+    def entropy(self) -> Tensor:
         """Entropy of dirichlet distribution
 
         Returns:
@@ -155,8 +163,8 @@ def entropy(self):
         return self._dirichlet.entropy()
 
     @property
-    def _natural_parameters(self):
+    def _natural_parameters(self) -> tuple[Tensor, Tensor]:
         return (self.alpha, self.beta)
 
-    def _log_normalizer(self, x, y):
+    def _log_normalizer(self, x: Tensor, y: Tensor) -> Tensor:
         return paddle.lgamma(x) + paddle.lgamma(y) - paddle.lgamma(x + y)