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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def load_batch_x(path_batch, flatten = False, time_major = False):
 """ Loads a batch of input features given a list of paths to numpy arrays in that batch."""


    utterances = [np.load(str(path)) for path in path_batch]
    utter_lens = [utterance.shape[0] for utterance in utterances]
    max_len = max(utter_lens)
    batch_size = len(path_batch)
    shape = (batch_size, max_len) + tuple(utterances[0].shape[1:])
    batch = np.zeros(shape)
    for i, utt in enumerate(utterances):
        batch[i] = zero_pad(utt, max_len)
    if flatten:
        batch = collapse(batch, time_major=time_major)
    return batch, np.array(utter_lens)