/model/train http API giving 500 error when providing “nlu” data in json

I am trying to train model using httpapi and json data blow is the code.

import requests
import json

data = {
    "config": "language: en\npipeline:\n- name: WhitespaceTokenizer\n- name: RegexFeaturizer\n- name: LexicalSyntacticFeaturizer\n- name: CountVectorsFeaturizer\n- name: CountVectorsFeaturizer\nanalyzer: \"char_wb\"\nmin_ngram: 1\nmax_ngram: 4\n- name: DIETClassifier\nepochs: 100\n- name: EntitySynonymMapper\n- name: ResponseSelector\nepochs: 100",
    "nlu": json.dumps({
        "rasa_nlu_data": {
            "regex_features": [],
            "entity_synonyms": [],
            "common_examples": [
                {
                    "text": "i m looking for a place to eat",
                            "intent": "restaurant_search",
                            "entities": []
                },
                {
                    "text": "I want to grab lunch",
                            "intent": "restaurant_search",
                            "entities": []
                },
                {
                    "text": "I am searching for a dinner spot",
                            "intent": "restaurant_search",
                            "entities": []
                },
            ]
        }
    }),
    "force": False,
    "save_to_default_model_directory": True
}

r = requests.post('http://localhost:5005/model/train', json=data)

It gives me 500 error. Below is the log for error:

2020-09-30 07:40:37,511 [DEBUG]  Traceback (most recent call last):
  File "/home/Documents/practice/rasa/test1/venv/lib/python3.6/site-packages/rasa/server.py", line 810, in train
    None, functools.partial(train_model, **info)
  File "/usr/lib/python3.6/concurrent/futures/thread.py", line 56, in run
    result = self.fn(*self.args, **self.kwargs)
  File "/home/Documents/practice/rasa/test1/venv/lib/python3.6/site-packages/rasa/train.py", line 50, in train
    additional_arguments=additional_arguments,
  File "uvloop/loop.pyx", line 1456, in uvloop.loop.Loop.run_until_complete
  File "/home/Documents/practice/rasa/test1/venv/lib/python3.6/site-packages/rasa/train.py", line 83, in train_async
    config, domain, training_files
  File "/home/Documents/practice/rasa/test1/venv/lib/python3.6/site-packages/rasa/importers/importer.py", line 79, in load_from_config
    config = io_utils.read_config_file(config_path)
  File "/home/Documents/practice/rasa/test1/venv/lib/python3.6/site-packages/rasa/utils/io.py", line 188, in read_config_file
    content = read_yaml(read_file(filename))
  File "/home/Documents/practice/rasa/test1/venv/lib/python3.6/site-packages/rasa/utils/io.py", line 124, in read_yaml
    return yaml_parser.load(content) or {}
  File "/home/Documents/practice/rasa/test1/venv/lib/python3.6/site-packages/ruamel/yaml/main.py", line 343, in load
    return constructor.get_single_data()
  File "/home/Documents/practice/rasa/test1/venv/lib/python3.6/site-packages/ruamel/yaml/constructor.py", line 111, in get_single_data
    node = self.composer.get_single_node()
  File "_ruamel_yaml.pyx", line 706, in _ruamel_yaml.CParser.get_single_node
  File "_ruamel_yaml.pyx", line 724, in _ruamel_yaml.CParser._compose_document
  File "_ruamel_yaml.pyx", line 775, in _ruamel_yaml.CParser._compose_node
  File "_ruamel_yaml.pyx", line 891, in _ruamel_yaml.CParser._compose_mapping_node
  File "_ruamel_yaml.pyx", line 904, in _ruamel_yaml.CParser._parse_next_event
ruamel.yaml.parser.ParserError: while parsing a block mapping
  in "<unicode string>", line 1, column 1
did not find expected key
  in "<unicode string>", line 11, column 1

When I train model using terminal commands and json file, it is trained successfully. I think I am missing some formatting required for /model/train api. Can someone tell me where am I going wrong?

I am using rasa version 1.10.14.

Thankyou in advance.

Turns out that the string in config was not proper. It was giving error when training model due to double quotes used with escape characters. I made some tweaks in the config and it trained the model successfully