我收到此错误：'''
InvalidArgumentError: Not enough time for target transition sequence (required: 138, available: 76)0You can turn this error into a warning by using the flag ignore_longer_outputs_than_inputs
[[node model_10/ctc/CTCLoss (defined at <ipython-input-211-3c40d9e71078>:3) ]] [Op:__inference_train_function_18256]
'''
这是我正在使用的代码，我可以把这个参数ignore_longer_outputs_than_inputs放在哪里？
'''
def ctc_lambda_func(args):
y_pred, labels, input_length, label_length = args
return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
def add_ctc_loss(input_to_softmax):
the_labels = Input(name='the_labels', shape=(None,), dtype='float32')
input_lengths = Input(name='input_length', shape=(1,), dtype='int64')
label_lengths = Input(name='label_length', shape=(1,), dtype='int64')
output_lengths = Lambda(input_to_softmax.output_length)(input_lengths)
# CTC loss is implemented in a lambda layer
loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')(
[input_to_softmax.output, the_labels, output_lengths, label_lengths])
model = Model(
inputs=[input_to_softmax.input, the_labels, input_lengths, label_lengths],
outputs=loss_out)
return model
'''