How to use the allennlp.data.fields.ListField function in allennlp

action_sequence = []
            elif action_sequence is None:
                return None

        index_fields: List[Field] = []
        production_rule_fields: List[Field] = []

        for production_rule in all_actions:
            nonterminal, _ = production_rule.split(" ->")
            production_rule = " ".join(production_rule.split(" "))
            field = ProductionRuleField(
                production_rule, self._world.is_global_rule(nonterminal), nonterminal=nonterminal
            )
            production_rule_fields.append(field)

        valid_actions_field = ListField(production_rule_fields)
        fields["valid_actions"] = valid_actions_field

        action_map = {
            action.rule: i  # type: ignore
            for i, action in enumerate(valid_actions_field.field_list)
        }

        for production_rule in action_sequence:
            index_fields.append(IndexField(action_map[production_rule], valid_actions_field))
        if not action_sequence:
            index_fields = [IndexField(-1, valid_actions_field)]

        action_sequence_field = ListField(index_fields)
        fields["action_sequence"] = action_sequence_field
        return Instance(fields)

targets : ``List[str]``, optional
            Contains the target tokens to be predicted.  The length of this list should be the same
            as the number of [MASK] tokens in the input.
        """
        if not tokens:
            tokens = self._tokenizer.tokenize(sentence)
        input_field = TextField(tokens, self._token_indexers)
        mask_positions = []
        for i, token in enumerate(tokens):
            if token.text == "[MASK]":
                mask_positions.append(i)
        if not mask_positions:
            raise ValueError("No [MASK] tokens found!")
        if targets and len(targets) != len(mask_positions):
            raise ValueError(f"Found {len(mask_positions)} mask tokens and {len(targets)} targets")
        mask_position_field = ListField([IndexField(i, input_field) for i in mask_positions])
        fields: Dict[str, Field] = {"tokens": input_field, "mask_positions": mask_position_field}
        # TODO(mattg): there's a problem if the targets get split into multiple word pieces...
        # (maksym-del): if we index word that was not split into wordpieces with
        # PretrainedTransformerTokenizer we will get OOV token ID...
        # Until this is handeled, let's use first wordpiece id for each token since tokens should contain text_ids
        # to be indexed with PretrainedTokenIndexer. It also requeires hack to avoid adding special tokens...
        if targets is not None:
            # target_field = TextField([Token(target) for target in targets], self._token_indexers)
            first_wordpieces = [self._targets_tokenizer.tokenize(target)[0] for target in targets]
            target_tokens = []
            for wordpiece, target in zip(first_wordpieces, targets):
                target_tokens.append(
                    Token(text=target, text_id=wordpiece.text_id, type_id=wordpiece.type_id)
                )
            fields["target_ids"] = TextField(target_tokens, self._token_indexers)
        return Instance(fields)

if target is not None:
            fields['target'] = TextField(_tokenize(target), self._token_indexers)
            metadata['target_tokens'] = target
        if shortlist is not None:
            fields['shortlist'] = TextField(_tokenize(shortlist), self._entity_indexers)
        if raw_entity_ids is not None:
            fields['raw_entity_ids'] = TextField(_tokenize(raw_entity_ids), self._raw_entity_indexers)
        if entity_ids is not None:
            fields['entity_ids'] = TextField(_tokenize(entity_ids), self._entity_indexers)
        if parent_ids is not None:
            fields['parent_ids'] = ListField([
                TextField(_tokenize(sublist),
                          token_indexers=self._entity_indexers)
                for sublist in parent_ids])
        if relations is not None:
            fields['relations'] = ListField([
                TextField(_tokenize(sublist),
                          token_indexers=self._relation_indexers)
                for sublist in relations])
        if mention_type is not None:
            fields['mention_type'] = SequentialArrayField(mention_type, dtype=np.int64)
        if shortlist_inds is not None:
            fields['shortlist_inds'] = SequentialArrayField(shortlist_inds, dtype=np.int64)
        if alias_copy_inds is not None:
            fields['alias_copy_inds'] = SequentialArrayField(alias_copy_inds, dtype=np.int64)

        return Instance(fields)

fields['s_ent_def'] = TextField(
            self._tokenizer.tokenize(s_ent['definition']), self._token_only_indexer
        ) if s_ent['definition'] else self._empty_token_text_field
        fields['t_ent_def'] = TextField(
            self._tokenizer.tokenize(t_ent['definition']), self._token_only_indexer
        ) if t_ent['definition'] else self._empty_token_text_field

        # add entity context fields
        s_contexts = sample_n(s_ent['other_contexts'], 16, 256)
        t_contexts = sample_n(t_ent['other_contexts'], 16, 256)

        fields['s_ent_context'] = ListField(
            [TextField(self._tokenizer.tokenize(c), self._token_only_indexer)
             for c in s_contexts]
        )
        fields['t_ent_context'] = ListField(
            [TextField(self._tokenizer.tokenize(c), self._token_only_indexer)
             for c in t_contexts]
        )

        # add boolean label (0 = no match, 1 = match)
        fields['label'] = BooleanField(label)

        return Instance(fields)

question_span_fields.append(SpanField(-1, -1, question_passage_field))
            fields["answer_as_question_spans"] = ListField(question_span_fields)
            
            if self.exp_search == 'add_sub':
                add_sub_signs_field: List[Field] = []
                extra_signs_field: List[Field] = []
                for signs_for_one_add_sub_expressions in valid_expressions:
                    extra_signs = signs_for_one_add_sub_expressions[:len(self.extra_numbers)]
                    normal_signs = signs_for_one_add_sub_expressions[len(self.extra_numbers):]
                    add_sub_signs_field.append(SequenceLabelField(normal_signs, numbers_in_passage_field))
                    extra_signs_field.append(SequenceLabelField(extra_signs, extra_numbers_field))
                if not add_sub_signs_field:
                    add_sub_signs_field.append(SequenceLabelField([0] * len(number_tokens), numbers_in_passage_field))
                if not extra_signs_field:
                    extra_signs_field.append(SequenceLabelField([0] * len(self.extra_numbers), extra_numbers_field))
                fields["answer_as_expressions"] = ListField(add_sub_signs_field)
                if self.extra_numbers:
                    fields["answer_as_expressions_extra"] = ListField(extra_signs_field)
            elif self.exp_search in ['template', 'full']:
                expression_indices = []
                for expression in valid_expressions:
                    if not expression:
                        expression.append(3 * [-1])
                    expression_indices.append(ArrayField(np.array(expression), padding_value=-1))
                if not expression_indices:
                    expression_indices = \
                        [ArrayField(np.array([3 * [-1]]), padding_value=-1) for _ in range(len(self.templates))]
                fields["answer_as_expressions"] = ListField(expression_indices)

            count_fields: List[Field] = [LabelField(count_label, skip_indexing=True) for count_label in valid_counts]
            if not count_fields:
                count_fields.append(LabelField(-1, skip_indexing=True))

def text_to_instance(self,  # type: ignore
                         item_id: Any,
                         question_text: str,
                         choice_text_list: List[str],
                         answer_id: int
                         ) -> Instance:
        # pylint: disable=arguments-differ
        fields: Dict[str, Field] = {}
        question_tokens = self._tokenizer.tokenize(question_text)
        choices_tokens_list = [self._tokenizer.tokenize(x) for x in choice_text_list]
        fields['question'] = TextField(question_tokens, self._token_indexers)
        fields['choices_list'] = ListField([TextField(x, self._token_indexers) for x in choices_tokens_list])
        fields['label'] = LabelField(answer_id, skip_indexing=True)

        metadata = {
            "id": item_id,
            "question_text": question_text,
            "choice_text_list": choice_text_list,
            "question_tokens": [x.text for x in question_tokens],
            "choice_tokens_list": [[x.text for x in ct] for ct in choices_tokens_list],
        }

        fields["metadata"] = MetadataField(metadata)

        return Instance(fields)

def _process_sentence(self, sent: Sentence):
        # Get the sentence text and define the `text_field`.
        sentence_text = [self._normalize_word(word) for word in sent.text]
        text_field = TextField([Token(word) for word in sentence_text], self._token_indexers)

        # Enumerate spans.
        spans = []
        for start, end in enumerate_spans(sentence_text, max_span_width=self._max_span_width):
            spans.append(SpanField(start, end, text_field))
        span_field = ListField(spans)
        span_tuples = [(span.span_start, span.span_end) for span in spans]

        # Convert data to fields.
        # NOTE: The `ner_labels` and `coref_labels` would ideally have type
        # `ListField[SequenceLabelField]`, where the sequence labels are over the `SpanField` of
        # `spans`. But calling `as_tensor_dict()` fails on this specific data type. Matt G
        # recognized that this is an AllenNLP API issue and suggested that represent these as
        # `ListField[ListField[LabelField]]` instead.
        dataset = sent.doc.dataset
        fields = {}
        fields["text"] = text_field
        fields["spans"] = span_field
        if sent.ner is not None:
            ner_labels = self._process_ner(span_tuples, sent)
            fields["ner_labels"] = ListField(
                [LabelField(entry, label_namespace=f"ner_labels")

# Depending on the type of supervision used for training the parser, we may want either
        # target action sequences or an agenda in our instance. We check if target sequences are
        # provided, and include them if they are. If not, we'll get an agenda for the sentence, and
        # include that in the instance.
        if target_sequences:
            action_sequence_fields: List[Field] = []
            for target_sequence in target_sequences:
                index_fields = ListField(
                    [
                        IndexField(instance_action_ids[action], action_field)
                        for action in target_sequence
                    ]
                )
                action_sequence_fields.append(index_fields)
                # TODO(pradeep): Define a max length for this field.
            fields["target_action_sequences"] = ListField(action_sequence_fields)
        elif self._output_agendas:
            # TODO(pradeep): Assuming every world gives the same agenda for a sentence. This is true
            # now, but may change later too.
            agenda = worlds[0].get_agenda_for_sentence(sentence)
            assert agenda, "No agenda found for sentence: %s" % sentence
            # agenda_field contains indices into actions.
            agenda_field = ListField(
                [IndexField(instance_action_ids[action], action_field) for action in agenda]
            )
            fields["agenda"] = agenda_field
        if labels:
            labels_field = ListField(
                [LabelField(label, label_namespace="denotations") for label in labels]
            )
            fields["labels"] = labels_field

if answer_texts is None or len(answer_texts) > 0:
        metadata['answer_texts'] = answer_texts
    else:
        metadata['answer_texts'] = ['']


    if token_spans:
        # There may be multiple answer annotations, so we pick the one that occurs the most.  This
        # only matters on the SQuAD dev set, and it means our computed metrics ("start_acc",
        # "end_acc", and "span_acc") aren't quite the same as the official metrics, which look at
        # all of the annotations.  This is why we have a separate official SQuAD metric calculation
        # (the "em" and "f1" metrics use the official script).
        candidate_answers: Counter = Counter()
        token_spans = set(token_spans)
        span_fields = []
        span_fields = ListField([SpanField(start, end, passage_field)
                                          for start, end in token_spans])
    else:
        span_fields = ListField([SpanField(-1, -1, passage_field)])

    fields['spans'] = span_fields
    metadata.update(additional_metadata)
    fields['metadata'] = MetadataField(metadata)
    return Instance(fields)

fields["answer_as_expressions_extra"] = ListField(extra_signs_field)
            elif self.exp_search in ['template', 'full']:
                expression_indices = []
                for expression in valid_expressions:
                    if not expression:
                        expression.append(3 * [-1])
                    expression_indices.append(ArrayField(np.array(expression), padding_value=-1))
                if not expression_indices:
                    expression_indices = \
                        [ArrayField(np.array([3 * [-1]]), padding_value=-1) for _ in range(len(self.templates))]
                fields["answer_as_expressions"] = ListField(expression_indices)

            count_fields: List[Field] = [LabelField(count_label, skip_indexing=True) for count_label in valid_counts]
            if not count_fields:
                count_fields.append(LabelField(-1, skip_indexing=True))
            fields["answer_as_counts"] = ListField(count_fields)
            
            fields["num_spans"] = LabelField(num_spans, skip_indexing=True)
        
        fields["metadata"] = MetadataField(metadata)
        
        return Instance(fields)