[architectures] fixed docstrings

bob/learn/pytorch/architectures/CASIANet.py

-
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -25,11 +24,11 @@ class CASIANet(nn.Module):
     The number of classes.
   drop_rate: float
     The probability for dropout.
-  conv: pyclass torch.nn.Module
+  conv: :py:class:`torch.nn.Module`
     The output of the convolutional / maxpool layers
-  avgpool: pyclass torch.nn.Module
+  avgpool: :py:class:`torch.nn.Module`
     The output of the average pooling layer (used as embedding) 
-  classifier: pyclass torch.nn.Module
+  classifier: :py:class:`torch.nn.Module`
     The output of the last linear (logits)
 
   """ 
@@ -58,13 +57,13 @@ class CASIANet(nn.Module):
 
     Parameters
     ----------
-    x: torch Tensor 
-        The data to forward through the network
+    x: :py:class:`torch.Tensor` 
+      The data to forward through the network
 
     Returns
     -------
-    x: torch Tensor 
-        The last layer of the network
+    x: :py:class:`torch.Tensor` 
+      The last layer of the network
     
     """
 

bob/learn/pytorch/architectures/CNN8.py

@@ -15,12 +15,12 @@ class CNN8(nn.Module):
       The number of classes.
   drop_rate: float
       The probability for dropout.
-  conv: :py:class:: torch.nn.Module
-      The output of the convolutional / maxpool layers
-  avgpool: :py:class:: torch.nn.Module
-      The output of the average pooling layer (used as embedding) 
-  classifier: :py:class:: torch.nn.Module
-      The output of the last linear (logits)
+  conv: :py:class:`torch.nn.Module`
+    The output of the convolutional / maxpool layers
+  avgpool: :py:class:`torch.nn.Module`
+    The output of the average pooling layer (used as embedding) 
+  classifier: :py:class:`torch.nn.Module`
+    The output of the last linear (logits)
 
   """ 
 
@@ -48,13 +48,13 @@ class CNN8(nn.Module):
 
     Parameters
     ----------
-    x: torch Tensor 
-        The data to forward through the network
+    x: :py:class:`torch.Tensor` 
+      The data to forward through the network
 
     Returns
     -------
-    x: torch Tensor 
-        The last layer of the network
+    x: :py:class:`torch.Tensor` 
+      The last layer of the network
     
     """