[Docs] Add Explaination of zero_infinity into CTCLoss() (#76188)

Author: aztice

python/paddle/nn/layer/loss.py

@@ -1292,6 +1292,7 @@ class CTCLoss(Layer):
         - input_lengths (Tensor): The length for each input sequence, it should have shape [batch_size] and dtype int64.
         - label_lengths (Tensor): The length for each label sequence, it should have shape [batch_size] and dtype int64.
         - norm_by_times (bool, optional): Whether to normalize the gradients by the number of time-step, which is also the sequence's length. There is no need to normalize the gradients if reduction mode is 'mean'. Default: False.
+        - zero_infinity (bool, optional): If True, set infinite loss to zero. Default: False.
 
     Returns:
         Tensor, The Connectionist Temporal Classification (CTC) loss between ``log_probs`` and  ``labels``. If attr:`reduction` is ``'none'``, the shape of loss is [batch_size], otherwise, the shape of loss is []. Data type is the same as ``log_probs``.