Rephrase a commentand delete one

Suggestions from @rchao

tensorflow/python/keras/callbacks.py

@@ -1172,15 +1172,15 @@ class LearningRateScheduler(Callback):
   ```python
   # Firstly, let's create a function which 
   # maps a given epoch to a learning rate.
-  # This function would keep the learning rate 
-  # constant at 0.001 for the first ten epochs and  
-  # let it decrease exponentially after that.
+  # This function keeps the learning rate 
+  # at 0.001 for the first ten epochs and  
+  # decreases it exponentially after that.
   def scheduler(epoch):
     if epoch < 10:
       return 0.001
     else:
       return 0.001 * tf.math.exp(0.1 * (10 - epoch))
-  # Next, we need to set up the callback and train the model.
+  
   callback = tf.keras.callbacks.LearningRateScheduler(scheduler)
   model.fit(data, labels, epochs=100, callbacks=[callback], 
             validation_data=(val_data, val_labels))