OSError: ai4bharat/IndicNER does not appear to have a file named tf_model.h5 but there is a file for PyTorch weights. Use `from_pt=True` to load this model from those weights

I am trying to integrate ai4bharat/IndicNER but it is throwing this error. I am not sure where to include this ‘from_pt = True’. I am using this custom component where I have loaded the TF weights. but it does not work when I put it in the config file. The error

My config file looks like this

- name: HFTransformersNLP
  model_name: "bert"
  model_weights: "ai4bharat/IndicNER"
  from_pt: True
- name: LanguageModelTokenizer
  intent_tokenization_flag: False
  intent_split_symbol: " "
- name: CountVectorsFeaturizer
  analyzer: char_wb
  min_ngram: 1
  max_ngram: 4
  OOV_token: _oov_
  use_shared_vocab: False
- name: KeywordIntentClassifier
  case_sensitive: True
- name: RegexFeaturizer
- name: LexicalSyntacticFeaturizer
- name: DIETClassifier
- name: LanguageModelFeaturizer
- name: test_ner.BertEntityExtractor
- name: CRFEntityExtractor
- name: EntitySynonymMapper
- name: ResponseSelector
  epochs: 100

# Configuration for Rasa Core.
# https://rasa.com/docs/rasa/core/policies/
- name: MemoizationPolicy
- name: TEDPolicy
  max_history: 5
  epochs: 100
- name: MappingPolicy
- name: FallbackPolicy
  nlu_threshold: 0.37
  core_threshold: 0.3
  fallback_action_name: action_default_fallback
  ambiguity_threshold: 0.1

My custom component file

import typing
from typing import Any, Dict, List, Text, Optional, Type

from transformers import pipeline
import torch
from transformers import AutoTokenizer, AutoModelForTokenClassification, TFAutoModelForTokenClassification

from rasa.shared.nlu.constants import ENTITIES
from rasa.nlu.components import Component
from rasa.nlu.config import RasaNLUModelConfig
from rasa.shared.nlu.training_data.message import Message
from rasa.shared.nlu.training_data.training_data import TrainingData
from rasa.nlu.extractors.extractor import EntityExtractor
from rasa.nlu.utils.hugging_face.hf_transformers import HFTransformersNLP

if typing.TYPE_CHECKING:
    from rasa.nlu.model import Metadata

tokenizer = AutoTokenizer.from_pretrained("ai4bharat/IndicNER")

model = TFAutoModelForTokenClassification.from_pretrained("ai4bharat/IndicNER", from_pt = True)
nlp = pipeline('ner', model=model, tokenizer=tokenizer)