cabbage972/GitChameleon-2.0 huggingface dataset:
Library: numpy==1.21.0 (Python 3.10, released 2021-06)

Problem: Write a function that rounds an array of numbers.

Starting code:
import numpy as np

def custom_round(arr:np.ndarray) -> np.ndarray:
    return 

Solution:
np.round_(arr)

Test:

def test_custom_round():
    arr = np.array([1.5, 2.3, 3.7])
    result = custom_round(arr)
    expected = np.array([2.0, 2.0, 4.0])
    assert np.array_equal(result, expected)

test_custom_round()
Library: numpy==1.21.0 (Python 3.10, released 2021-06)

Problem: Write a function that computes the product of an array.

Starting code:
import numpy as np

def custom_product(arr:np.ndarray) -> np.ndarray:
    return 

Solution:
np.product(arr)

Test:


def test_custom_product():
    arr = np.array([1, 2, 3, 4])
    result = custom_product(arr)
    expected = 24
    assert result == expected

test_custom_product()
Library: numpy==1.21.0 (Python 3.10, released 2021-06)

Problem: Write a function that computes the cumulative product of an array.

Starting code:
import numpy as np

def custom_cumproduct(arr:np.ndarray) -> np.ndarray:
    return 

Solution:
np.cumproduct(arr)

Test:

def test_custom_cumproduct():
    arr = np.array([1, 2, 3, 4])
    result = custom_cumproduct(arr)
    expected = np.array([1, 2, 6, 24])
    assert np.array_equal(result, expected)

test_custom_cumproduct()
Library: numpy==1.21.0 (Python 3.10, released 2021-06)

Problem: Write a function that checks if any elements in an array are true.

Starting code:
import numpy as np

def custom_anytrue(arr:np.ndarray) -> np.ndarray:
    return 

Solution:
np.sometrue(arr)

Test:

def test_custom_sometrue():
    arr = np.array([0, 0, 1, 0])
    result = custom_anytrue(arr)
    expected = True
    assert result == expected

test_custom_sometrue()
Library: numpy==1.21.0 (Python 3.10, released 2021-06)

Problem: Write a function that checks if all elements in an array are true.

Starting code:
import numpy as np

def custom_alltrue(arr:np.ndarray) -> np.ndarray:
    return 

Solution:
np.alltrue(arr)

Test:


def test_custom_alltrue():
    arr = np.array([1, 1, 1, 1])
    result = custom_alltrue(arr)
    expected = True
    assert result == expected

test_custom_alltrue()
Library: lightgbm==3.0.0 (Python 3.10, released 2020-08)
Additional dependencies: numpy==1.26.4 scikit-learn==1.3.2

Problem: Perform cross-validation with the given parameters and return the evaluation history for each fold.

Starting code:
import numpy as np
import lightgbm as lgb
from sklearn.datasets import make_classification

NUM_SAMPLES = 500
NUM_FEATURES = 20
INFORMATIVE_FEATURES = 2
REDUNDANT_FEATURES = 10
RANDOM_STATE = 42
NUM_BOOST_ROUND = 100
NFOLD = 5
LEARNING_RATE = 0.05
EARLY_STOPPING_ROUNDS = 10
X, y = make_classification(n_samples=NUM_SAMPLES, n_features=NUM_FEATURES, n_informative=INFORMATIVE_FEATURES, n_redundant=REDUNDANT_FEATURES, random_state=RANDOM_STATE)
train_data = lgb.Dataset(X, label=y)

params = {
    'objective': 'binary',
    'metric': 'binary_logloss',
    'learning_rate': LEARNING_RATE,
    'verbose': -1
}

cv_results = lgb.cv(
    params=params,
    train_set=train_data,
    num_boost_round=NUM_BOOST_ROUND,
    nfold=NFOLD,
    early_stopping_rounds=EARLY_STOPPING_ROUNDS,

Solution:
return_cvbooster=True
)

Test:
import numpy as np
assertion_1_value = 'cvbooster' in cv_results
assertion_2_value = len(cv_results['cvbooster'].boosters) == NFOLD
assertion_3_value = all(isinstance(booster, lgb.Booster) for booster in cv_results['cvbooster'].boosters)
assert assertion_1_value
assert assertion_2_value
assert assertion_3_value
Library: lightgbm==3.0.0 (Python 3.10, released 2020-08)

Problem: Write a function to decode a byte string.

Starting code:
import lightgbm.compat as compat
def decode_string(string: bytes) -> str:
    return 

Solution:
compat.decode_string(string)

Test:
ENCODED_STRING = b'\x68\x65\x6c\x6c\x6f'
expected = 'hello'
assert decode_string(ENCODED_STRING) == expected
Library: lightgbm==3.0.0 (Python 3.10, released 2020-08)
Additional dependencies: numpy==1.26.4 scikit-learn==1.3.2

Problem: Perform cross-validation with the given parameters and display the training metric in progress.

Starting code:
import numpy as np
import lightgbm as lgb
from sklearn.datasets import make_classification

NUM_SAMPLES = 500
NUM_FEATURES = 20
INFORMATIVE_FEATURES = 2
REDUNDANT_FEATURES = 10
RANDOM_STATE = 42
NUM_BOOST_ROUND = 100
NFOLD = 5
LEARNING_RATE = 0.05
EARLY_STOPPING_ROUNDS = 10
X, y = make_classification(n_samples=NUM_SAMPLES, n_features=NUM_FEATURES, n_informative=INFORMATIVE_FEATURES, n_redundant=REDUNDANT_FEATURES, random_state=RANDOM_STATE)
train_data = lgb.Dataset(X, label=y)

params = {
    'objective': 'binary',
    'metric': 'binary_logloss',
    'learning_rate': LEARNING_RATE,
    'verbose': -1
}

cv_results = lgb.cv(
    params=params,
    train_set=train_data,
    num_boost_round=NUM_BOOST_ROUND,
    nfold=NFOLD,
    early_stopping_rounds=EARLY_STOPPING_ROUNDS,

Solution:
eval_train_metric=True
)

Test:
assertion_value = {'train binary_logloss-mean', 'train binary_logloss-stdv', 'valid binary_logloss-mean', 'valid binary_logloss-stdv'}.issubset(cv_results.keys())
assert assertion_value
Library: lightgbm==3.0.0 (Python 3.10, released 2020-08)
Additional dependencies: numpy==1.26.4

Problem: Write a function to convert a ctypes pointer to a NumPy array of the specified length.

Starting code:
import lightgbm as lgb
import numpy as np
import ctypes

def convert_cint32_array_to_numpy(c_pointer: ctypes.POINTER, length: int) -> np.ndarray:
    """
    Convert a ctypes pointer to a numpy array.
    
    Args:
        c_pointer (c_array_type): A ctypes pointer to an array of integers.
        length (int): The length of the array.
        
    Returns:
        np.ndarray: A numpy array containing the elements of the ctypes array.
    """
    return lgb

Solution:
.basic.cint32_array_to_numpy(c_pointer, length)

Test:
c_array_type = ctypes.POINTER(ctypes.c_int32)
c_array = (ctypes.c_int32 * 5)(1, 2, 3, 4, 5)
c_pointer = ctypes.cast(c_array, c_array_type)
length = 5
np_array = convert_cint32_array_to_numpy(c_pointer, length)
assertion_1_value = isinstance(np_array, np.ndarray)
assertion_2_value = np_array.shape == (5,)
assertion_3_value = np.array_equal(np_array, np.array([1, 2, 3, 4, 5], dtype=np.int32))
assert assertion_1_value
assert assertion_2_value
assert assertion_3_value
Library: lightgbm==3.0.0 (Python 3.10, released 2020-08)
Additional dependencies: numpy==1.26.4

Problem: Write a function to get the parameters of a dataset object as a dictionary.

Starting code:
import lightgbm as lgb
import numpy as np

def get_params(dataset: lgb.Dataset) -> dict:
    """
    Get the parameters of the dataset.
    
    Args:
        dataset (lgb.Dataset): The dataset to get the parameters from.
        
    Returns:
        dict: The parameters of the dataset.
    """
    return 

Solution:
dataset.get_params()

Test:
data = np.random.rand(10, 2)
label = np.random.randint(2, size=10)
dataset = lgb.Dataset(data, label=label)

params = get_params(dataset)
assertion_value= isinstance(params, dict) or params is None
assert assertion_value
Library: lightgbm==3.0.0 (Python 3.10, released 2020-08)
Additional dependencies: numpy==1.26.4

Problem: Write a function to serialize a NumPy array to a JSON string using a custom default function that converts NumPy arrays to lists.

Starting code:
import numpy as np
import json
from lightgbm.compat import json_default_with_numpy

def dump_json(data: any) -> str:
    """
    Dump data to JSON format.
    
    Args:
        data (any): The data to dump.
        
    Returns:
        str: The JSON representation of the data.
    """
    return json.dumps(data

Solution:
, default=json_default_with_numpy)

Test:
NUMPY_ARRAY = np.array([1, 2, 3])
json_data = dump_json(NUMPY_ARRAY)
expected = '[1, 2, 3]'
assert json_data == expected
Library: lightgbm==4.3.0 (Python 3.10, released 2024-01)

Problem: Write a function to create a ctypes array from a list of values.

Starting code:
import ctypes
import lightgbm.basic as basic

def create_c_array(values: list, ctype: type) -> ctypes.Array:
    """
    Create a ctypes array from a list of values.
    Args:
        values (list): A list of values to be converted to a ctypes array.
        ctype (type): The ctypes type of the array elements.
    Returns:
        ctypes.Array: A ctypes array containing the values.
    """
    return 

Solution:
basic._c_array(ctype, values)

Test:
CTYPE = ctypes.c_double
VALUES = [0.1, 0.2, 0.3, 0.4, 0.5]
c_array = create_c_array(VALUES, CTYPE)
assertion_1_value = all(isinstance(i, float) for i in c_array)
assertion_2_value = all(c_array[i] == VALUES[i] for i in range(len(VALUES)))

assert assertion_1_value
assert assertion_2_value
Library: lightgbm==4.3.0 (Python 3.10, released 2024-01)

Problem: Write a function to convert a Python string to a C string.

Starting code:
import lightgbm as lgb
import ctypes

def c_str(python_string: str) -> ctypes.c_char_p:
    """
    Convert a Python string to a ctypes c_char_p.
    
    Args:
        python_string (str): The Python string to convert.
        
    Returns:
        ctypes.c_char_p: The converted ctypes c_char_p.
    """
    return 

Solution:
lgb.basic._c_str(python_string)

Test:
python_string = "lightgbm"
c_string = c_str(python_string)
assertion_1_value = isinstance(c_string, ctypes.c_char_p)
assertion_2_value =  c_string.value.decode('utf-8') == python_string
assert assertion_1_value
assert assertion_2_value
Library: lightgbm==4.3.0 (Python 3.10, released 2024-01)
Additional dependencies: numpy==1.26.4

Problem: Write a function to convert a sliced NumPy array back to a contiguous NumPy array.

Starting code:
import lightgbm as lgb
import numpy as np

def convert_from_sliced_object(sliced_data: np.ndarray) -> np.ndarray:
    """
    Convert a sliced object to a fixed object.
    
    Args:
        sliced_data (np.ndarray): The sliced object to convert.
        
    Returns:
        np.ndarray: The converted fixed object.
    """
    return lgb

Solution:
.basic._convert_from_sliced_object(sliced_data)

Test:
data = np.random.rand(100, 10)
sliced_data = data[:, :5]
fixed_data = convert_from_sliced_object(sliced_data)
assert isinstance(fixed_data, np.ndarray)
assert fixed_data.shape == sliced_data.shape
assert np.array_equal(fixed_data, sliced_data)
Library: spacy==3.5.0 (Python 3.10, released 2023-01)
Additional dependencies: numpy==1.26.4

Problem: Write a function to get the labels of the span ruler.

Starting code:
import spacy
from spacy.pipeline.span_ruler import SpanRuler

def get_labels(ruler: SpanRuler) -> tuple:
    """
    Get the labels of the SpanRuler.
    
    Args:
        ruler (SpanRuler): The SpanRuler to get the labels from.
        
    Returns:
        tuple: The labels of the SpanRuler.
    """
    return ruler

Solution:
.labels

Test:
nlp = spacy.blank("en")
ruler = SpanRuler(nlp)

patterns = [
    {"label": "PERSON", "pattern": [{"LOWER": "john"}]},
    {"label": "GPE", "pattern": [{"LOWER": "london"}]},
]
ruler.add_patterns(patterns)
labels = get_labels(ruler)
assert isinstance(labels, tuple)
expected = ('GPE', 'PERSON')
assert labels == expected
Library: spacy==3.5.0 (Python 3.10, released 2023-01)
Additional dependencies: numpy==1.26.4

Problem: Write a function to create a whitespace variant of an example.

Starting code:
import spacy
from spacy.training import Example
from spacy.training import augment

def create_whitespace_variant(nlp: spacy.Language, example: Example, whitespace: str, position: int) -> Example:
    """
    Create a whitespace variant of the given example.
    
    Args:
        nlp (Language): The spaCy language model.
        example (Example): The example to augment.
        whitespace (str): The whitespace to insert.
        position (int): The position to insert the whitespace.
        
    Returns:
        Example: The augmented example.
    """
    return 

Solution:
augment.make_whitespace_variant(nlp, example, whitespace, position)

Test:
nlp = spacy.blank("en")

tokens = nlp("Hello world")
annotations = {"entities": [(0, 5, "GREETING")]}
example = Example.from_dict(tokens, annotations)

whitespace = " "
position = 1

augmented_example = create_whitespace_variant(nlp, example, whitespace, position)
expected_doc_annotation = {
    'cats': {},
    'entities': ['U-GREETING', 'O', 'O'],
    'spans': {},
    'links': {}
}

expected_token_annotation = {
    'ORTH': ['Hello', ' ', 'world'],
    'SPACY': [True, False, False],
    'TAG': ['', '', ''],
    'LEMMA': ['', '', ''],
    'POS': ['', '', ''],
    'MORPH': ['', '', ''],
    'HEAD': [0, 1, 2],
    'DEP': ['', '', ''],
    'SENT_START': [1, 0, 0]
}

assert augmented_example.to_dict()["doc_annotation"] == expected_doc_annotation
assert augmented_example.to_dict()["token_annotation"] == expected_token_annotation
Library: spacy==3.5.0 (Python 3.10, released 2023-01)
Additional dependencies: numpy==1.26.4

Problem: Write a function to remove a pattern from a span ruler by its ID.

Starting code:
import spacy
from spacy.pipeline.span_ruler import SpanRuler


def remove_pattern_by_id(ruler: SpanRuler, pattern_id: str) -> None:
    """
    Remove a pattern from the SpanRuler by its ID.
    
    Args:
        ruler (SpanRuler): The SpanRuler to remove the pattern from.
        pattern_id (str): The ID of the pattern to remove.
        
    Returns:
        None
    """
    ruler

Solution:
.remove_by_id(pattern_id)

Test:
nlp = spacy.blank("en")
ruler = SpanRuler(nlp)

patterns = [
    {"label": "PERSON", "pattern": [{"LOWER": "john"}], "id": "pattern1"},
    {"label": "GPE", "pattern": [{"LOWER": "london"}], "id": "pattern2"},
]
ruler.add_patterns(patterns)

assert len(ruler.patterns) == 2

pattern_id_to_remove = "pattern1"

remove_pattern_by_id(ruler, pattern_id_to_remove)
assertion_value = len(ruler.patterns) == 1 
assert assertion_value
remaining_pattern_ids = [pattern["id"] for pattern in ruler.patterns]
assertion_value = pattern_id_to_remove not in remaining_pattern_ids
assert assertion_value
Library: nltk==3.7 (Python 3.10, released 2022-02)

Problem: Write a function to align words in a hypothesis and reference sentence using the METEOR algorithm.

Starting code:
import nltk
from nltk.stem import PorterStemmer
from nltk.corpus import wordnet

def align_words_func(hypothesis, reference):
    """
    Align words between hypothesis and reference sentences.
    
    Args:
        hypothesis (list): List of words in the hypothesis sentence.
        reference (list): List of words in the reference sentence.
        
    Returns:
        tuple: A tuple containing the aligned matches, unmatched hypothesis, and unmatched reference.
    """
    return 

Solution:
nltk.translate.meteor_score.align_words(hypothesis, reference)

Test:
hypothesis = ["the", "cat", "sits", "on", "the", "mat"]
reference = ["the", "cat", "is", "sitting", "on", "the", "mat"]
expected_matches = [(0, 0), (1, 1), (2, 3), (3, 4), (4, 5), (5, 6)]
matches, unmatched_hypo, unmatched_ref = align_words_func(hypothesis, reference)
val1 = matches == expected_matches
val2 = unmatched_hypo == []
val3 = unmatched_ref == [(2, 'is')]
assert val1
assert val2
assert val3
Library: nltk==3.7 (Python 3.10, released 2022-02)

Problem: Write a function to get examples of a synset from WordNet.

Starting code:
import nltk
nltk.download('wordnet')
nltk.download('omw-1.4')
from nltk.corpus import wordnet

def get_synset_examples(synset: str) -> list:
    """
    Get examples for a given synset.
    
    Args:
        synset (str): The synset to get examples for.
        
    Returns:
        list: A list of examples for the synset.
    """
    return wordnet.synset(synset)

Solution:
.examples()

Test:
synset = 'dog.n.01'
examples = get_synset_examples(synset)
assertion_value = isinstance(examples, list)
assert assertion_value
assertion_value = examples == ['the dog barked all night']
assert assertion_value
Library: nltk==3.7 (Python 3.10, released 2022-02)

Problem: Write a function to parse a string representation of a tree into an NLTK Tree object.

Starting code:
import nltk
nltk.download('sinica_treebank')
from nltk.tree import Tree
from nltk.corpus import sinica_treebank

def parse_sinica_treebank_sentence(sentence: str) -> Tree:
    """
    Parse a sentence from the Sinica Treebank.
    
    Args:
        sentence (str): The sentence to parse.
        
    Returns:
        Tree: The parsed tree.
    """
    return 

Solution:
Tree.fromstring(sentence)

Test:
sinica_sentence = sinica_treebank.parsed_sents()[0]
tree_string = sinica_sentence.pformat()

parsed_tree = parse_sinica_treebank_sentence(tree_string)
assertion_value =isinstance(parsed_tree, Tree)
assert assertion_value
assertion_value = parsed_tree.label() == "NP"
assert assertion_value
Library: nltk==3.5 (Python 3.10, released 2020-04)

Problem: Write a function to accumulate the results of applying a function to elements of an iterable.

Starting code:
from nltk.lm.api import accumulate
import operator

def accumulate_functional(iterable, func):
    """
    Accumulate the results of applying a function to an iterable.
    
    Args:
        iterable (iterable): An iterable to accumulate.
        func (function): A function to apply to the elements of the iterable.
        
    Returns:
        list: A list of accumulated results.
    """
    return list(

Solution:
accumulate(iterable, func))

Test:
iterable = [1, 2, 3, 4, 5]
func = operator.add
result = accumulate_functional(iterable, func)
assertion_value = isinstance(result, list)
assert assertion_value
assertion_value =  result == [1, 3, 6, 10, 15]
assert assertion_value
Library: nltk==3.5 (Python 3.10, released 2020-04)

Problem: Write a function to tokenize a string

Starting code:
import nltk.tokenize.destructive

def tokenize_sentence(sentence: str) -> list:
    """
    Tokenize a sentence into words.
    
    Args:
        sentence (str): The sentence to tokenize.
        
    Returns:
        list: A list of tokens.
    """
    return nltk

Solution:
.tokenize.destructive.NLTKWordTokenizer().tokenize(sentence)

Test:
sentence = "This is a test sentence."
tokens = tokenize_sentence(sentence)
assertion_value =isinstance(tokens, list)
assert assertion_value
assertion_value = tokens == ["This", "is", "a", "test", "sentence", "."]
assert assertion_value
Library: django==5.0.0 (Python 3.10, released 2023-12)

Problem: 
Complete the function that returns a datetime object with time zone settings to utc for the given datetime. If needed, use another library. Do not run the app in your code.

Starting code:
import django
from django.conf import settings
from django.utils import timezone

settings.configure()
def get_time_in_utc(year: int, month: int, day: int) -> timezone.datetime:


Solution:

    from datetime import timezone as py_timezone
    return timezone.datetime(year, month, day, tzinfo=py_timezone.utc)


Test:

year = 2024
month = 11
day = 5
utc_time = get_time_in_utc(year, month, day)
assertion_value = utc_time.tzname() == 'UTC'
assert assertion_value
assertion_value = utc_time.isoformat() == '2024-11-05T00:00:00+00:00'
assert assertion_value
Library: django==4.0.0 (Python 3.10, released 2021-12)

Problem: 
Complete the function that returns a datetime object with time zone settings to utc for the given datetime. If needed, use another library. Do not run the app in your code.

Starting code:
import django
from django.conf import settings
from django.utils import timezone

settings.configure()
def get_time_in_utc(year: int, month: int, day: int) -> timezone.datetime:


Solution:

    return timezone.datetime(year, month, day, tzinfo=timezone.utc)


Test:


year = 2024
month = 11
day = 5
utc_time = get_time_in_utc(year, month, day)
assertion_value = utc_time.tzname() == 'UTC'
assert assertion_value
assertion_value = utc_time.isoformat() == '2024-11-05T00:00:00+00:00'
assert assertion_value
Library: django==4.0.0 (Python 3.10, released 2021-12)

Problem: 
Complete the function save_existing. Do not run the app in your code.

Starting code:
from django.conf import settings
from django.forms.models import BaseModelFormSet
from django.forms.renderers import get_default_renderer
from django.forms import Form

settings.configure()
def save_existing(formset: BaseModelFormSet, form : Form, obj:str) -> None:


Solution:
    return formset.save_existing(form=form,instance=obj)


Test:


class DummyForm:
    def save(self, commit=True):
        return 'dummy_instance_value_result'

class MyFormSet(BaseModelFormSet):
    def __init__(self, *args, **kwargs):
        self.renderer = get_default_renderer()
        super().__init__(*args, **kwargs)
fs5 = MyFormSet(queryset=[])
result = save_existing(formset=fs5,form=DummyForm(), obj='dummy_str')
assertion_result = result == 'dummy_instance_value_result'
assert assertion_result
Library: django==5.0.0 (Python 3.10, released 2023-12)

Problem: 
Complete the function save_existing. Do not run the app in your code.

Starting code:
from django.conf import settings
from django.forms.models import BaseModelFormSet
from django.forms.renderers import get_default_renderer
from django.forms import Form

settings.configure()
def save_existing(formset: BaseModelFormSet, form : Form, instance:str) -> None:


Solution:
    return formset.save_existing(form=form,obj=instance)


Test:

class DummyForm:
    def save(self, commit=True):
        return 'dummy_instance_value_result'

class MyFormSet(BaseModelFormSet):
    def __init__(self, *args, **kwargs):
        self.renderer = get_default_renderer()
        super().__init__(*args, **kwargs)
fs5 = MyFormSet(queryset=[])
result = save_existing(formset=fs5,form=DummyForm(), instance='dummy_str')
assertion_result = result == 'dummy_instance_value_result'
assert assertion_result
Library: django==5.0.0 (Python 3.10, released 2023-12)

Problem: 
Complete the get_template_string function so that the template_string variable form the html string provided in comments, making the best use of the templates for form field rendering. Do not run the app in your code.


Starting code:
import django
from django.conf import settings
from django import forms
from django.template import Template, Context

settings.configure(
      TEMPLATES=[
          {
              'BACKEND': 'django.template.backends.django.DjangoTemplates',
          },
      ],
  )
django.setup()

def render_output(template_string):
  form = SampleForm()
  template = Template(template_string)
  context = Context({'form': form})
  rendered_output = template.render(context)
  return rendered_output

# target for html string
# <form>
#   <div>
#     <label for='id_name'>Name:</label>

# <div class='helptext' id='id_name_helptext'>Enter your name</div>

# <input type='text' name='name' required aria-describedby='id_name_helptext' id='id_name'>
#   </div>
# </form>

class SampleForm(forms.Form):
    name = forms.CharField(label='Name', help_text='Enter your name')
def get_template_string()->str:


Solution:
    return '''
<form>
  <div>
    {{ form.name.as_field_group }}
  </div>
</form>
'''

Test:

template_string= get_template_string()
rendered_output = render_output(template_string)
def normalize_html(html):
    # Remove all whitespace and standardize quotation marks to single quotes
    normalized = ''.join(html.split())
    return normalized

template_string_django_4 = '''
<form>
  <div>
    {{ form.name.label_tag }}
    {% if form.name.help_text %}
      <div class="helptext" id="{{ form.name.auto_id }}_helptext">
        {{ form.name.help_text|safe }}
      </div>
    {% endif %}
    {{ form.name.errors }}
    {{ form.name }}
  </div>
</form>
'''
assertion_result = normalize_html(rendered_output) == normalize_html(render_output(template_string))
assert assertion_result
assertion_result = len(template_string) < 100 # check if the template_string is not too long (ideally should be 278)
assert assertion_result