How to use the deeppavlov.models.coreference_resolution.old_model.custom_layers.shape function in deeppavlov

Create boolean mask for emb tensor.
        Args:
            emb: Some embeddings tensor with rank 2 or 3
            text_len_mask: A mask tensor representing the first N positions of each row.

        Returns: emb tensor after mask applications.

        """
        num_sentences = tf.shape(emb)[0]
        max_sentence_length = tf.shape(emb)[1]

        emb_rank = len(emb.get_shape())
        if emb_rank == 2:
            flattened_emb = tf.reshape(emb, [num_sentences * max_sentence_length])
        elif emb_rank == 3:
            flattened_emb = tf.reshape(emb, [num_sentences * max_sentence_length, custom_layers.shape(emb, 2)])
        else:
            raise ValueError("Unsupported rank: {}".format(emb_rank))
        return tf.boolean_mask(flattened_emb, text_len_mask)

List of predictions and scores, and Loss function value
        """
        self.dropout = 1 - (tf.cast(is_training, tf.float64) * self.dropout_rate)
        self.lexical_dropout = 1 - (tf.cast(is_training, tf.float64) * self.lexical_dropout_rate)

        num_sentences = tf.shape(word_emb)[0]
        max_sentence_length = tf.shape(word_emb)[1]

        text_emb_list = [word_emb]

        if self.char_embedding_size > 0:
            char_emb = tf.gather(
                tf.get_variable("char_embeddings", [len(self.char_dict), self.char_embedding_size]),
                char_index)  # [num_sentences, max_sentence_length, max_word_length, emb]
            flattened_char_emb = tf.reshape(char_emb, [num_sentences * max_sentence_length,
                                                       custom_layers.shape(char_emb, 2),
                                                       custom_layers.shape(char_emb, 3)])
            # [num_sentences * max_sentence_length, max_word_length, emb]

            flattened_aggregated_char_emb = custom_layers.cnn(flattened_char_emb, self.filter_widths, self.filter_size)
            # [num_sentences * max_sentence_length, emb]

            aggregated_char_emb = tf.reshape(flattened_aggregated_char_emb,
                                             [num_sentences,
                                              max_sentence_length,
                                              custom_layers.shape(flattened_aggregated_char_emb, 1)])
            # [num_sentences, max_sentence_length, emb]

            text_emb_list.append(aggregated_char_emb)

        text_emb = tf.concat(text_emb_list, 2)
        text_emb = tf.nn.dropout(text_emb, self.lexical_dropout)

"""
        Forms a new tensor using special features, mentions embeddings, mentions scores, etc.
        and passes it through a fully-connected network that compute antecedent scores.
        Args:
            mention_emb: [num_mentions, emb], a tensor that contains of embeddings of specific mentions
            mention_scores: [num_mentions, 1], Output of the fully-connected network, that compute the mentions scores.
            antecedents: [] get from C++ function
            antecedents_len: [] get from C++ function
            mention_speaker_ids: [num_mentions, speaker_emb_size], tf.float64, Speaker IDs.
            genre_emb: [genre_emb_size], tf.float64, Genre

        Returns: tf.float64, [num_mentions, max_ant + 1], antecedent scores.

        """
        num_mentions = custom_layers.shape(mention_emb, 0)
        max_antecedents = custom_layers.shape(antecedents, 1)

        feature_emb_list = []

        if self.use_metadata:
            antecedent_speaker_ids = tf.gather(mention_speaker_ids, antecedents)  # [num_mentions, max_ant]
            same_speaker = tf.equal(tf.expand_dims(mention_speaker_ids, 1),
                                    antecedent_speaker_ids)  # [num_mentions, max_ant]
            speaker_pair_emb = tf.gather(tf.get_variable("same_speaker_emb", [2, self.feature_size],
                                                         dtype=tf.float64),
                                         tf.to_int32(same_speaker))  # [num_mentions, max_ant, emb]
            feature_emb_list.append(speaker_pair_emb)

            tiled_genre_emb = tf.tile(tf.expand_dims(tf.expand_dims(genre_emb, 0), 0),
                                      [num_mentions, max_antecedents, 1])  # [num_mentions, max_ant, emb]
            feature_emb_list.append(tiled_genre_emb)

mention_emb_list.append(mention_end_emb)

        mention_width = 1 + mention_ends - mention_starts  # [num_mentions]
        if self.use_features:
            mention_width_index = mention_width - 1  # [num_mentions]
            mention_width_emb = tf.gather(tf.get_variable("mention_width_embeddings", [self.max_mention_width,
                                                                                       self.feature_size],
                                                          dtype=tf.float64),
                                          mention_width_index)  # [num_mentions, emb]
            mention_width_emb = tf.nn.dropout(mention_width_emb, self.dropout)
            mention_emb_list.append(mention_width_emb)

        if self.model_heads:
            mention_indices = tf.expand_dims(tf.range(self.max_mention_width), 0) + tf.expand_dims(
                mention_starts, 1)  # [num_mentions, max_mention_width]
            mention_indices = tf.minimum(custom_layers.shape(text_outputs, 0) - 1,
                                         mention_indices)  # [num_mentions, max_mention_width]
            mention_text_emb = tf.gather(text_emb, mention_indices)  # [num_mentions, max_mention_width, emb]

            self.head_scores = custom_layers.projection(text_outputs, 1)  # [num_words, 1]

            mention_head_scores = tf.gather(self.head_scores, mention_indices)  # [num_mentions, max_mention_width, 1]
            mention_mask = tf.expand_dims(tf.sequence_mask(mention_width, self.max_mention_width, dtype=tf.float64), 2)
            # [num_mentions, max_mention_width, 1]

            mention_attention = tf.nn.softmax(mention_head_scores + tf.log(mention_mask),
                                              dim=1)  # [num_mentions, max_mention_width, 1]
            mention_head_emb = tf.reduce_sum(mention_attention * mention_text_emb, 1)  # [num_mentions, emb]
            mention_emb_list.append(mention_head_emb)

        mention_emb = tf.concat(mention_emb_list, 1)  # [num_mentions, emb]
        return mention_emb

"""
        Forms a new tensor using special features, mentions embeddings, mentions scores, etc.
        and passes it through a fully-connected network that compute antecedent scores.
        Args:
            mention_emb: [num_mentions, emb], a tensor that contains of embeddings of specific mentions
            mention_scores: [num_mentions, 1], Output of the fully-connected network, that compute the mentions scores.
            antecedents: [] get from C++ function
            antecedents_len: [] get from C++ function
            mention_speaker_ids: [num_mentions, speaker_emb_size], tf.float64, Speaker IDs.
            genre_emb: [genre_emb_size], tf.float64, Genre

        Returns: tf.float64, [num_mentions, max_ant + 1], antecedent scores.

        """
        num_mentions = custom_layers.shape(mention_emb, 0)
        max_antecedents = custom_layers.shape(antecedents, 1)

        feature_emb_list = []

        if self.use_metadata:
            antecedent_speaker_ids = tf.gather(mention_speaker_ids, antecedents)  # [num_mentions, max_ant]
            same_speaker = tf.equal(tf.expand_dims(mention_speaker_ids, 1),
                                    antecedent_speaker_ids)  # [num_mentions, max_ant]
            speaker_pair_emb = tf.gather(tf.get_variable("same_speaker_emb", [2, self.feature_size],
                                                         dtype=tf.float64),
                                         tf.to_int32(same_speaker))  # [num_mentions, max_ant, emb]
            feature_emb_list.append(speaker_pair_emb)

            tiled_genre_emb = tf.tile(tf.expand_dims(tf.expand_dims(genre_emb, 0), 0),
                                      [num_mentions, max_antecedents, 1])  # [num_mentions, max_ant, emb]
            feature_emb_list.append(tiled_genre_emb)

Create boolean mask for emb tensor.
        Args:
            emb: Some embeddings tensor with rank 2 or 3
            text_len_mask: A mask tensor representing the first N positions of each row.

        Returns: emb tensor after mask applications.

        """
        num_sentences = tf.shape(emb)[0]
        max_sentence_length = tf.shape(emb)[1]

        emb_rank = len(emb.get_shape())
        if emb_rank == 2:
            flattened_emb = tf.reshape(emb, [num_sentences * max_sentence_length])
        elif emb_rank == 3:
            flattened_emb = tf.reshape(emb, [num_sentences * max_sentence_length, custom_layers.shape(emb, 2)])
        else:
            raise ValueError("Unsupported rank: {}".format(emb_rank))
        return tf.boolean_mask(flattened_emb, text_len_mask)

if self.char_embedding_size > 0:
            char_emb = tf.gather(
                tf.get_variable("char_embeddings", [len(self.char_dict), self.char_embedding_size]),
                char_index)  # [num_sentences, max_sentence_length, max_word_length, emb]
            flattened_char_emb = tf.reshape(char_emb, [num_sentences * max_sentence_length,
                                                       custom_layers.shape(char_emb, 2),
                                                       custom_layers.shape(char_emb, 3)])
            # [num_sentences * max_sentence_length, max_word_length, emb]

            flattened_aggregated_char_emb = custom_layers.cnn(flattened_char_emb, self.filter_widths, self.filter_size)
            # [num_sentences * max_sentence_length, emb]

            aggregated_char_emb = tf.reshape(flattened_aggregated_char_emb,
                                             [num_sentences,
                                              max_sentence_length,
                                              custom_layers.shape(flattened_aggregated_char_emb, 1)])
            # [num_sentences, max_sentence_length, emb]

            text_emb_list.append(aggregated_char_emb)

        text_emb = tf.concat(text_emb_list, 2)
        text_emb = tf.nn.dropout(text_emb, self.lexical_dropout)

        text_len_mask = tf.sequence_mask(text_len, maxlen=max_sentence_length)
        text_len_mask = tf.reshape(text_len_mask, [num_sentences * max_sentence_length])

        text_outputs = self.encode_sentences(text_emb, text_len, text_len_mask)
        text_outputs = tf.nn.dropout(text_outputs, self.dropout)  # [num_sentences * max_sentence_length, emb] (my)

        genre_emb = tf.gather(tf.get_variable("genre_embeddings",
                                              [len(self.genres), self.feature_size],
                                              dtype=tf.float64),