sanity check and missing doctest modules

Author: autoih

tensorflow_addons/metrics/cohens_kappa.py

@@ -36,7 +36,7 @@ class CohenKappa(Metric):
     while calculating the Cohen's Kappa score.
 
     Usage:
-    
+
     >>> actuals = np.array([4, 4, 3, 4, 2, 4, 1, 1], dtype=np.int32)
     >>> preds = np.array([4, 4, 3, 4, 4, 2, 1, 1], dtype=np.int32)
     >>> weights = np.array([1, 1, 2, 5, 10, 2, 3, 3], dtype=np.int32)

tensorflow_addons/metrics/multilabel_confusion_matrix.py

@@ -47,10 +47,10 @@ class MultiLabelConfusionMatrix(Metric):
     - true positives for class i in M(1,1)
 
     Usage:
-    
-    >>> y_true = tf.constant([[1, 0, 1], [0, 1, 0]], dtype=tf.int32)	
+
+    >>> y_true = tf.constant([[1, 0, 1], [0, 1, 0]], dtype=tf.int32)
     >>> y_pred = tf.constant([[1, 0, 0],[0, 1, 1]], dtype=tf.int32)
-    >>> output1 = tfa.metrics.MultiLabelConfusionMatrix(num_classes=3)	
+    >>> output1 = tfa.metrics.MultiLabelConfusionMatrix(num_classes=3)
     >>> output1.update_state(y_true, y_pred)
     >>> output1.result()
     <tf.Tensor: shape=(3, 2, 2), dtype=float32, numpy=
@@ -62,10 +62,10 @@ class MultiLabelConfusionMatrix(Metric):
     <BLANKLINE>
            [[0., 1.],
             [1., 0.]]], dtype=float32)>
-    >>> y_true = tf.constant([[1, 0, 0], [0, 1, 0]], dtype=tf.int32)	
-    >>> y_pred = tf.constant([[1, 0, 0],[0, 0, 1]], dtype=tf.int32)	
-    >>> output2 = tfa.metrics.MultiLabelConfusionMatrix(num_classes=3)	
-    >>> output2.update_state(y_true, y_pred)	
+    >>> y_true = tf.constant([[1, 0, 0], [0, 1, 0]], dtype=tf.int32)
+    >>> y_pred = tf.constant([[1, 0, 0],[0, 0, 1]], dtype=tf.int32)
+    >>> output2 = tfa.metrics.MultiLabelConfusionMatrix(num_classes=3)
+    >>> output2.update_state(y_true, y_pred)
     >>> output2.result()
     <tf.Tensor: shape=(3, 2, 2), dtype=float32, numpy=
     array([[[1., 0.],