modified sample code of CTCLoss & ctc_loss by remove disable_static() & print([.*].numpy()) & alias, test=document_fix (#28403)

python/paddle/nn/functional/loss.py

@@ -1112,7 +1112,6 @@ def ctc_loss(log_probs,
             input_lengths = np.array([5, 5]).astype("int64")
             label_lengths = np.array([3, 3]).astype("int64")
 
-            paddle.disable_static()
             log_probs = paddle.to_tensor(log_probs)
             labels = paddle.to_tensor(labels)
             input_lengths = paddle.to_tensor(input_lengths)
@@ -1123,14 +1122,14 @@ def ctc_loss(log_probs,
                 label_lengths,
                 blank=0,
                 reduction='none')
-            print(loss.numpy())  #[3.9179852 2.9076521]
+            print(loss)  #[3.9179852 2.9076521]
 
             loss = F.ctc_loss(log_probs, labels,
                 input_lengths,
                 label_lengths,
                 blank=0,
                 reduction='mean')
-            print(loss.numpy())  #[1.1376063]
+            print(loss)  #[1.1376063]
 
     """
 

python/paddle/nn/layer/loss.py

@@ -883,8 +883,6 @@ class MarginRankingLoss(fluid.dygraph.Layer):
 
 class CTCLoss(fluid.dygraph.Layer):
     """
-	:alias_main: paddle.nn.CTCLoss
-	:alias: paddle.nn.CTCLoss, paddle.nn.layer.CTCLoss, paddle.nn.layer.loss.CTCLoss
 
     An operator integrating the open source Warp-CTC library (https://github.com/baidu-research/warp-ctc)
     to compute Connectionist Temporal Classification (CTC) loss.
@@ -941,7 +939,6 @@ class CTCLoss(fluid.dygraph.Layer):
             input_lengths = np.array([5, 5]).astype("int64")
             label_lengths = np.array([3, 3]).astype("int64")
 
-            paddle.disable_static()
             log_probs = paddle.to_tensor(log_probs)
             labels = paddle.to_tensor(labels)
             input_lengths = paddle.to_tensor(input_lengths)
@@ -950,12 +947,12 @@ class CTCLoss(fluid.dygraph.Layer):
             loss = paddle.nn.CTCLoss(blank=0, reduction='none')(log_probs, labels,
                 input_lengths,
                 label_lengths)
-            print(loss.numpy())  #[3.9179852 2.9076521]
+            print(loss)  #[3.9179852 2.9076521]
 
             loss = paddle.nn.CTCLoss(blank=0, reduction='mean')(log_probs, labels,
                 input_lengths,
                 label_lengths)
-            print(loss.numpy())  #[1.1376063]
+            print(loss)  #[1.1376063]
     """
 
     def __init__(self, blank=0, reduction='mean'):