update some comments of api

mindspore/nn/probability/bnn_layers/bnn_cell_wrapper.py

@@ -25,6 +25,9 @@ class ClassWrap:
     def __init__(self, cls):
         self._cls = cls
         self.bnn_loss_file = None
+        self.__doc__ = cls.__doc__
+        self.__name__ = cls.__name__
+        self.__bases__ = cls.__bases__
 
     def __call__(self, backbone, loss_fn, dnn_factor, bnn_factor):
         obj = self._cls(backbone, loss_fn, dnn_factor, bnn_factor)

mindspore/nn/probability/dpn/vae/cvae.py

@@ -31,7 +31,7 @@ class ConditionalVAE(Cell):
 
     Note:
         When define the encoder and decoder, the shape of the encoder's output tensor and decoder's input tensor
-        should be :math:`(N, hidden_size)`.
+        should be :math:`(N, hidden\_size)`.
         The latent_size should be less than or equal to the hidden_size.
 
     Args:
@@ -42,8 +42,8 @@ class ConditionalVAE(Cell):
         num_classes(int): The number of classes.
 
     Inputs:
-        - **input_x** (Tensor) - the same shape as the input of encoder.
+        - **input_x** (Tensor) - the same shape as the input of encoder, the shape is :math:`(N, C, H, W)`.
-        - **input_y** (Tensor) - the tensor of the target data, the shape is :math:`(N, 1)`.
+        - **input_y** (Tensor) - the tensor of the target data, the shape is :math:`(N,)`.
 
     Outputs:
         - **output** (tuple) - (recon_x(Tensor), x(Tensor), mu(Tensor), std(Tensor)).
@@ -99,7 +99,7 @@ class ConditionalVAE(Cell):
         Randomly sample from latent space to generate sample.
 
         Args:
-            sample_y (Tensor): Define the label of sample, int tensor.
+            sample_y (Tensor): Define the label of sample, int tensor, the shape is (generate_nums, ).
             generate_nums (int): The number of samples to generate.
             shape(tuple): The shape of sample, it should be (generate_nums, C, H, W) or (-1, C, H, W).
 
@@ -121,8 +121,8 @@ class ConditionalVAE(Cell):
         Reconstruct sample from original data.
 
         Args:
-            x (Tensor): The input tensor to be reconstructed.
+            x (Tensor): The input tensor to be reconstructed, the shape is (N, C, H, W).
-            y (Tensor): The label of the input tensor.
+            y (Tensor): The label of the input tensor, the shape is (N,).
 
         Returns:
             Tensor, the reconstructed sample.

mindspore/nn/probability/dpn/vae/vae.py

@@ -29,7 +29,7 @@ class VAE(Cell):
 
     Note:
         When define the encoder and decoder, the shape of the encoder's output tensor and decoder's input tensor
-        should be :math:`(N, hidden_size)`.
+        should be :math:`(N, hidden\_size)`.
         The latent_size should be less than or equal to the hidden_size.
 
     Args:
@@ -39,7 +39,7 @@ class VAE(Cell):
         latent_size(int): The size of the latent space.
 
     Inputs:
-        - **input** (Tensor) - the same shape as the input of encoder.
+        - **input** (Tensor) - the same shape as the input of encoder, the shape is :math:`(N, C, H, W)`.
 
     Outputs:
         - **output** (Tuple) - (recon_x(Tensor), x(Tensor), mu(Tensor), std(Tensor)).
@@ -106,7 +106,7 @@ class VAE(Cell):
         Reconstruct sample from original data.
 
         Args:
-            x (Tensor): The input tensor to be reconstructed.
+            x (Tensor): The input tensor to be reconstructed, the shape is (N, C, H, W).
 
         Returns:
             Tensor, the reconstructed sample.

mindspore/nn/probability/infer/variational/elbo.py

@@ -37,7 +37,7 @@ class ELBO(Cell):
 
     Inputs:
         - **input_data** (Tuple) - (recon_x(Tensor), x(Tensor), mu(Tensor), std(Tensor)).
-        - **target_data** (Tensor) - the target tensor.
+        - **target_data** (Tensor) - the target tensor of shape :math:`(N,)`.
 
     Outputs:
         Tensor, loss float tensor.

tests/st/probability/test_gpu_svi_cvae.py

@@ -98,7 +98,7 @@ def create_dataset(data_path, batch_size=32, repeat_size=1,
     return mnist_ds
 
 
-def test_svi_cave():
+def test_svi_cvae():
     # define the encoder and decoder
     encoder = Encoder(num_classes=10)
     decoder = Decoder()