what's the mean of input_length in K.ctc_batch_cost()
CTC loss for one example is calculated on a 2D array (T,C). C must be equal to number of character + 1(blank characters). C contains the probability distribution of the characters at a time stamps. T will be the number of time stamps. 
T should be of length 2* max_string_length. All possible encoding of y_true with length T will be used in negative log loss calculation.
It is usually the shape of previous layer output.
I have download a code for ocr using Keras, which applied the CRNN network and use the CTC loss as the loss function. 
However, I'm really new to CTC loss and just have trouble with the usage of K.ctc_batch_cost(), especially the meaning of input_length. In the document of keras, 

Arguments of tf.keras.backend.ctc_batch_cost(
      y_true,
      y_pred,
      input_length,
      label_length
  ) 


y_true: tensor (samples, max_string_length) containing the truth labels.
y_pred: tensor (samples, time_steps, num_categories) containing the prediction, or output of the softmax.
input_length: tensor (samples, 1) containing the sequence length for each batch item in y_pred.
label_length: tensor (samples, 1) containing the sequence length for each batch item in y_true.
However, my problem is just what's the meaning of input_length? is that the dimensional of the output of LSTM?