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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 build_char_states(self, char_embed, is_training, reuse, char_ids, char_lengths):
 """Build char embedding network for the QA model."""

        max_char_length = self.cfg.max_char_length

        inputs = dropout(tf.nn.embedding_lookup(char_embed, char_ids),
                         self.cfg.dropout, is_training)
        inputs = tf.reshape(
            inputs, shape=[max_char_length, -1, self.cfg.char_embed_dim])
        char_lengths = tf.reshape(char_lengths, shape=[-1])
        with tf.variable_scope('char_encoding', reuse=reuse):
            cell_fw = XGRUCell(hidden_dim=self.cfg.char_embed_dim)
            cell_bw = XGRUCell(hidden_dim=self.cfg.char_embed_dim)
            _, (left_right, right_left) = tf.nn.bidirectional_dynamic_rnn(
                cell_fw=cell_fw,
                cell_bw=cell_bw,
                sequence_length=char_lengths,
                inputs=inputs,
                time_major=True,
                dtype=tf.float32
            )

        left_right = tf.reshape(left_right, shape=[-1, self.cfg.char_embed_dim])

        right_left = tf.reshape(right_left, shape=[-1, self.cfg.char_embed_dim])

        states = tf.concat([left_right, right_left], axis=1)
        out_shape = tf.shape(char_ids)[1:3]
        out_shape = tf.concat([out_shape, tf.constant(
            value=[self.cfg.char_embed_dim * 2], dtype=tf.int32)], axis=0)
        return tf.reshape(states, shape=out_shape)