result:
def extract_video_id(url):
    parsed_url = urlparse(url)
    query = parsed_url.query
    query_dict = parse_qs(query)
    video_id = query_dict['v'][0]
    return video_id


result:
def connect_ssh(hostname, port, username, password):
    client = paramiko.SSHClient()
    client.load_system_host_keys()
    client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
    client.connect(hostname, port, username=username, password=password)
    return client


result:
def get_speech_service():
    credentials, project_id = google.auth.default()
    if credentials.requires_scopes:
        credentials = credentials.with_scopes(['https://www.googleapis.com/auth/cloud-platform'])
    else:
        credentials = Credentials.from_authorized_user_info(info=None)
    service = build('speech', 'v1', credentials=credentials)
    return service


result:
def delete_dataset(dataset_id):
    credentials, project_id = google.auth.default()
    if credentials.requires_scopes:
        credentials = credentials.with_scopes(['https://www.googleapis.com/auth/cloud-platform'])
    else:
        credentials = Credentials.from_authorized_user_info(info=None)
    service = build('bigquery', 'v2', credentials=credentials)
    service.datasets().delete(projectId=project_id, datasetId=dataset_id).execute()


result:
def set_dag_access_policy(dag_id, role):
    dag_model = DagModel.get_dagmodel(dag_id)
    dag_model_view = DagModel.DagModelView.set_role(dag_model, role)
    dag_model_view.save()


result:
def generate_halton_points(num_points, num_dimensions):
    # Generate the Halton sequence
    points = halton_sequence(num_dimensions, num_points, seed=12345)

    # Scale the points to the desired range
    points = points * 2 - 1

    return points


result:
def validate_block_size(block_size, desired_block_size):
    if not isinstance(block_size, int) or block_size <= 0:
        raise ValueError("Block size must be a positive integer")
    if block_size % desired_block_size != 0:
        raise ValueError("Block size must be a multiple of {}".format(desired_block_size))


result:
def maybe_call_fn_and_grads(fn, grads, *args, **kwargs):
    if fn is not None:
        result = fn(*args, **kwargs)
        if grads is not None:
            grads_result = grads(*args, **kwargs)
            return result, grads_result
        else:
            return result
    else:
        return None


result:
def degree_vector(input_list):
    # Create a dictionary to store the counts of each element
    counts = {}

    # Iterate over the input list and count the occurrences of each element
    for element in input_list:
        if element in counts:
            counts[element] += 1
        else:
            counts[element] = 1

    # Return the counts as a list
    return list(counts.values())


result:
def fixed_dropout(inputs, rate, fixed_mask_dims):
    # Generate a drop mask with the same value for all fixed dimensions
    mask = np.random.rand(*inputs.shape) > rate
    for dim in fixed_mask_dims:
        mask[:, dim] = True

    # Apply the drop mask to the inputs
    return inputs * mask


result:
def create_word2vec_estimator(model_path):
    # Load the external model
    model = h2o.load_model(model_path)

    # Create the H2OWord2vecEstimator
    estimator = H2OWord2vecEstimator(model=model)

    return estimator


result:
def reconstruction_error(test_data, reconstructor):
    # Reconstruct the input
    reconstructed = reconstructor(test_data)

    # Calculate the difference between the input and its reconstruction
    error = np.sum(np.abs(test_data - reconstructed))

    return error


result:
async def defer_message(message):
    # Defer the message
    await asyncio.sleep(0)

    # Return the message
    return message


result:
def delete_image(image_id):
    # Create an EC2 client
    ec2 = boto3.client('ec2')

    # Delete the image
    ec2.delete_image(ImageId=image_id)


result:
def create_message_from_response(response):
    # Extract the relevant information from the response
    status_code = response.status_code
    content = response.text

    # Format the message
    message = "Response: {}\nContent: {}".format(status_code, content)

    return message


result:
def find_episode_by_experience_id(experience_id, episodes):
    for episode in episodes:
        if episode['experience_id'] == experience_id:
            return episode
    return None


result:
def sort_dataframe(df, by):
    # Sort the DataFrame by the given expression
    sorted_df = df.sort_values(by=by)

    return sorted_df


result:
def spectral_bandwidth(psd, p):
    # Calculate the p'th-order spectral moment
    moment = np.sum(psd * np.arange(len(psd))**p)

    # Calculate the p'th-order spectral bandwidth
    bandwidth = np.sqrt(moment / np.sum(psd))

    return bandwidth


result:
def add_requirements_from_setup_cfg(metadata_path):
    # Parse the setup.cfg file
    config = configparser.ConfigParser()
    config.read('setup.cfg')

    # Get the requirements from the setup.cfg file
    requirements = config.get('options', 'install_requires').split('\n')

    # Load the file metadata
    with open(metadata_path, 'r') as f:
        metadata = json.load(f)

    # Add the requirements to the file metadata
    metadata['requires'].extend(requirements)

    # Save the updated file metadata
    with open(metadata_path, 'w') as f:
        json.dump(metadata, f)


result:
def is_square_matrix(array):
    # Check if the number of rows and columns are equal
    return array.shape[0] == array.shape[1]


result:
def reset_qubit(qubit):
    # Create a quantum circuit
    qc = qiskit.QuantumCircuit(qubit.register)

    # Apply the reset instruction
    qc.reset(qubit)

    # Run the circuit
    backend = qiskit.Aer.get_backend('qasm_simulator')
    job = qiskit.execute(qc, backend)
    result = job.result()

    # Return the resulting qubit
    return result.get_statevector(qc)[0]


result:
def create_backend_config_widget():
    # Create a widget
    widget = PyQt5.QWidget()

    # Set the widget layout
    layout = PyQt5.QVBoxLayout()
    widget.setLayout(layout)

    # Create a label and line edit for the backend name
    name_label = PyQt5.QLabel('Backend Name:')
    name_edit = PyQt5.QLineEdit()
    layout.addWidget(name_label)
    layout.addWidget(name_edit)

    # Create a label and line edit for the backend provider
    provider_label = PyQt5.QLabel('Backend Provider:')
    provider_edit = PyQt5.QLineEdit()
    layout.addWidget(provider_label)
    layout.addWidget(provider_edit)

    # Return the widget
    return widget


result:
def to_quantum_channel_or_operator(input):
    # Check if the input is a QuantumChannel subclass
    if isinstance(input, qiskit.quantum_info.QuantumChannel):
        return input

    # Check if the input is a numpy array
    if isinstance(input, np.ndarray):
        # Check if the input is a unitary matrix
        if np.allclose(np.dot(input, input.conj().T), np.eye(input.shape[0])):
            return qiskit.quantum_info.Operator(input)
        else:
            raise ValueError("Input is not a unitary matrix.")

    raise ValueError("Input is not a QuantumChannel subclass or unitary matrix.")


result:
def transform_results(results, transform_fn):
    # Apply the transformation function to the results
    transformed_results = transform_fn(results)

    # Create a dictionary with the output names as keys
    output_names = [output.name for output in results.outputs]
    return {name: value for name, value in zip(output_names, transformed_results)}


result:
def relabel_expression(expr, mapping_fn):
    # Create a copy of the expression
    expr_clone = expr.copy()

    # Get the variables in the expression
    variables = expr_clone.free_symbols

    # Modify the labels of the variables using the mapping function
    for variable in variables:
        variable_label = mapping_fn(variable.name)
        expr_clone = expr_clone.xreplace({variable: sympy.Symbol(variable_label)})

    return expr_clone


result:
def add_metadata(item, metadata):
    # Update the metadata of the item
    item.update(metadata)

    return item


result:
def parse_observation_result(result):
    # Load the result element as a JSON object
    result_json = json.loads(result)

    # Extract the relevant information
    name = result_json['name']
    value = result_json['value']

    # Return the parsed result
    return (name, value)


result:
def search_project(project, criteria):
    # Filter the items in the project based on the criteria
    matching_items = [item for item in project if all(item.get(key) == value for key, value in criteria.items())]

    return matching_items


result:
def list_check_groups():
    # Get the check groups
    check_groups = pylint.checkers.BaseChecker.groups

    # Return the check groups
    return check_groups


result:
def check_node_line_number(node, line_number, flag):
    # Check the line number of the node
    if node.lineno == line_number:
        # Perform the action if the flag is not set
        if not flag:
            print("Line number matches.")
            flag = True

    return flag


result:
def copy_revoked_object(num_copies):
    # Create an empty Revoked object
    revoked = type('Revoked', (), {})()

    # Create a list to store the copies
    copies = []

    # Copy the Revoked object repeatedly
    for i in range(num_copies):
        copy_revoked = copy.deepcopy(revoked)
        copies.append(copy_revoked)

    return copies


result:
def expand_file_path(file_path):
    # Expand environment variables
    file_path = os.path.expandvars(file_path)

    # Expand user home directory
    file_path = os.path.expanduser(file_path)

    return file_path


result:
def create_voronoi_quatrefoil_torus_knot():
    # Create a quatrefoil (3,4) torus knot
    u = np.linspace(0, 2 * np.pi, 100)
    v = np.linspace(0, 2 * np.pi, 100)
    u, v = np.meshgrid(u, v)
    x, y, z = torus_knot(3, 4, u, v)

    # Create a voronoi diagram
    points = np.array([[x, y, z] for x, y, z in zip(x.flatten(), y.flatten(), z.flatten())])
    vor = voronoi_diagram(points)

    # Create a rainbow colormap
    cmap = color_map('rainbow')

    # Plot the voronoi quatrefoil torus knot
    plot_surface(x, y, z, color=cmap(vor.point_region / np.max(vor.point_region)), alpha=0.5)


result:
def acquire_lock(lock):
    # Attempt to acquire the lock
    acquired = lock.acquire(blocking=False)

    return acquired


result:
def is_track_saved(spotify, track_ids):
    # Check if the tracks are saved
    saved_tracks = spotify.current_user_saved_tracks_contains(track_ids)

    return saved_tracks


result:
def is_import_multiline(import_stmt):
    # Parse the import statement
    tree = ast.parse(import_stmt)

    # Check if the import spans multiple lines
    multiline = False
    for node in ast.iter_child_nodes(tree):
        if isinstance(node, ast.Import):
            if node.col_offset > 0:
                multiline = True
                break

    return multiline


result:
def derive_key(password, salt, key_length, n, r, p):
    # Create an Scrypt key derivation function
    kdf = Scrypt(
        salt=salt,
        length=key_length,
        n=n,
        r=r,
        p=p
    )

    # Derive the key
    key = kdf.derive(password)

    return key


result:
def fits_in_payload(new_span, max_payload_size):
    # Check if the new span fits in the payload
    fits = len(new_span) <= max_payload_size

    return fits


result:
def set_is_confirmed(bundle, is_confirmed):
    # Set the is_confirmed attribute
    bundle.is_confirmed = is_confirmed


result:
def get_rule(rules, cat, name):
    # Iterate over the rules in the category
    for rule in rules[cat]:
        if rule['name'] == name:
            return rule

    # Return None if no matching rule was found
    return None


result:
def pack_array(sub_field_array, mask):
    # Initialize the packed array
    packed_array = []

    # Iterate over the mask
    for i, include in enumerate(mask):
        if include:
            # Add the element from the sub field's array to the packed array if the mask element is True
            packed_array.append(sub_field_array[i])

    return packed_array


result:
def add_failure_callback(callback):
    # Define a function that will be called upon failure
    def wrapper():
        try:
            # Call the function
            function_to_call()
        except:
            # Call the callback upon failure
            callback()

    return wrapper


result:
def unsubscribe_ticker(ws, pair):
    # Unsubscribe from the ticker channel
    ws.unsubscribe(f'ticker.{pair}')


result:
def header_to_dict(header):
    # Initialize the dictionary
    header_dict = CaseInsensitiveDict()

    # Split the header into individual values
    values = header.split(',')

    # Add each value to the dictionary
    for value in values:
        key, _, val = value.strip().partition('=')
        header_dict.add(key, val)

    return header_dict


result:
def spawn_server_process(manager):
    def server_process():
        # Set up the queues for communication with the server process
        sender = Sender(manager.address, authkey=manager.authkey)
        receiver = Receiver(manager.address, authkey=manager.authkey)

        while True:
            try:
                # Receieve a command from the main process
                command = receiver.get()

                # Process the command
                if command[0] == 'stop':
                    break
                elif command[0] == 'call':
                    sender.put(manager._callmethod(*command[1:]))
            except (EOFError, QueueError, Full, Empty, ConnectionError):
                break

    # Spawn the server process
    p = Process(target=server_process)
    p.start()


result:
def separate_filename_line_number(string):
    # Split the string on the last ':' character
    parts = string.rsplit(':', 1)

    # Return the filename and line number as a tuple
    return (parts[0], int(parts[1]))


result:
def sgd_optimize(loss, params, learning_rate):
    # Define the updates for the parameters
    updates = [(param, param - learning_rate * T.grad(loss, param)) for param in params]

    # Define the optimization function
    optimize = theano.function(inputs=[], outputs=loss, updates=updates)

    return optimize


result:
def get_onset_offset_times(signal, fs):
    # Find the indices of the onsets or offsets
    indices = np.nonzero(signal)[1]

    # Convert the indices to times using the sample rate
    times = indices / fs

    return times


result:
def values_are_equal(v1, v2):
    # Check that the vectors have the same length
    if len(v1) != len(v2):
        return False

    # Compare the elements of the vectors
    return all([v1[i] == v2[i] for i in range(len(v1))])


result:
def load_config(default_config_path):
    # Create an argument parser
    parser = argparse.ArgumentParser()
    parser.add_argument('--config', help='Path to the configuration file')

    # Parse the command line arguments
    args = parser.parse_args()

    # Use the specified config file path, or the default if none was specified
    config_path = args.config if args.config is not None else default_config_path

    # Read the configuration file
    config = configparser.ConfigParser()
    config.read(config_path)

    return config


result:
def download_image_layer(url, download_folder):
    # Download the image layer
    response = requests.get(url, stream=True)

    # Save the image layer to a file in the download folder
    with open(download_folder / 'image_layer.tar.gz', 'wb') as f:
        shutil.copyfileobj(response.raw, f)

    # Extract the image layer
    shutil.unpack_archive(download_folder / 'image_layer.tar.gz', extract_dir=download_folder)

    # Remove the .tar.gz file
    (download_folder / 'image_layer.tar.gz').unlink()


result:
def should_add_color(image, threshold):
    # Convert the image to grayscale
    grayscale_image = np.mean(image, axis=-1)

    # Calculate the average intensity of the grayscale image
    avg_intensity = np.mean(grayscale_image)

    # Return True if the average intensity is below the threshold, False otherwise
    return avg_intensity < threshold


result:
def generate_relation_matrix(terms, relation_func):
    # Create an empty boolean matrix with the same number of rows and columns as the number of terms
    matrix = np.empty((len(terms), len(terms)), dtype=bool)

    # Fill the matrix with values indicating whether there is a relation between each pair of terms
    for i, term_i in enumerate(terms):
        for j, term_j in enumerate(terms):
            matrix[i, j] = relation_func(term_i, term_j)

    return matrix


result:
def add_dependency(parent_process_name, child_process_name):
    # Get the process object for the parent process
    parent_process = psutil.Process(name=parent_process_name)

    # Get the process object for the child process
    child_process = psutil.Process(name=child_process_name)

    # Add the child process as a dependency of the parent process
    parent_process.add_child(child_process)


result:
def create_filtered_dataframes(nodes, edges, exclude_nodes):
    # Create a DataFrame of nodes that does not include the specified nodes
    filtered_nodes = nodes[~nodes['node_id'].isin(exclude_nodes)]

    # Create a DataFrame of edges that does not include the specified nodes
    filtered_edges = edges[~edges['source'].isin(exclude_nodes) & ~edges['target'].isin(exclude_nodes)]

    return filtered_nodes, filtered_edges


result:
def estimate_value(variation, rate):
    # Calculate the estimated value using the previously calculated variation and the current rate
    value = variation / rate

    return value


result:
def display_structural_variant(variants, index):
    # Get the structural variant at the specified index
    variant = variants[index]

    # Print the structural variant information
    print(f"Chromosome: {variant['chromosome']}")
    print(f"Start position: {variant['start']}")
    print(f"End position: {variant['end']}")
    print(f"Type: {variant['type']}")


result:
def get_case_info(archive, case_id):
    # Find the case with the specified case ID in the archive
    for case in archive:
        if case['id'] == case_id:
            # Return the case information
            return case

    # Return None if the case is not found in the archive
    return None


result:
def vcf2cytosure(vcf_data, individual_id):
    # Initialize an empty list to store the CGH data
    cgh_data = []

    # Iterate through the VCF data
    for record in vcf_data:
        # Check if the individual is present in the record
        if individual_id in record['samples']:
            # Get the genotype of the individual
            genotype = record['samples'][individual_id]['genotype']

            # Convert the genotype to a CGH value
            if genotype == '0/0':
                cgh_value = 'normal'
            elif genotype == '0/1':
                cgh_value = 'loss'
            elif genotype == '1/1':
                cgh_value = 'gain'
            else:
                cgh_value = 'unknown'

            # Add the CGH value to the CGH data list
            cgh_data.append({'chromosome': record['chromosome'], 'position': record['position'], 'cgh_value': cgh_value})

    return cgh_data


result:
def read_block(file):
    # Read the header line
    header = file.readline().strip()

    # Initialize an empty list to store the data
    data = []

    # Read the data lines
    line = file.readline()
    while line and not line.startswith('#'):
        data.append(line.strip())
        line = file.readline()

    return header, data


result:
def check_unexpected_args(args, allowed_args):
    # Get the list of unexpected arguments
    unexpected_args = [arg for arg in args if arg not in allowed_args]

    # Check if there are any unexpected arguments
    if unexpected_args:
        # Print an error message and exit
        print(f"Error: Unexpected argument(s) found: {', '.join(unexpected_args)}")
        exit(1)


result:
def start_capture(capture_dir, capture_name, capture_duration):
    # Create the capture directory if it does not exist
    if not os.path.exists(capture_dir):
        os.makedirs(capture_dir)

    # Create the capture file path
    capture_file = os.path.join(capture_dir, f"{capture_name}.pcap")

    # Check if the capture file already exists
    if os.path.exists(capture_file):
        # Print an error message and exit
        print(f"Error: Capture file {capture_file} already exists")
        exit(1)

    # Start the capture process
    os.system(f"tcpdump -i eth0 -w {capture_file} -G {capture_duration} -W 1")


result:
def get_parser(type):
    # Define a dictionary of parser functions for different types
    parsers = {
        'int': int,
        'float': float,
        'str': str,
        'bool': bool,
    }

    # Return the parser function for the given type
    return parsers[type]


result:
def store_refs(objects, file_path):
    # Open the file for writing in binary mode
    with open(file_path, 'wb') as f:
        # Pickle the objects and write them to the file
        pickle.dump(objects, f)

    # Collect garbage
    gc.collect()


result:
def get_storage_dict():
    # Create and return an empty dictionary
    return {}


result:
def text_to_category(text):
    # Create a Category instance
    category = tasks.Category(text)

    # Return the Category instance
    return category


result:
def get_security_config(resource_name):
    # Load the security configuration from a file or database
    security_config = load_security_config()

    # Return the security configuration for the specified resource
    return security_config[resource_name]


result:
def get_value(namespace, name_sym):
    # Return the value bound to the name in the namespace
    return namespace[name_sym]


result:
def round_mantissa(number, digits):
    # Round the mantissa of the number to the given number of digits
    return round(number, digits - int(math.floor(math.log10(abs(number)))))


result:
def get_tag_parameter(tag_name, param_name):
    # Load the tags and their parameters from a file or database
    tags = load_tags()

    # Get the parameters for the specified tag
    tag_params = tags[tag_name]

    # Return the value of the specified parameter
    return tag_params[param_name]


result:
def escape_query_keys(url):
    # Split the URL into the base URL and the query string
    base_url, query_string = url.split('?')

    # Split the query string into individual keys and values
    query_pairs = query_string.split('&')

    # Escape the keys in each query pair
    escaped_pairs = [urllib.parse.quote(key) + '=' + value for key, value in query_pairs]

    # Join the escaped query pairs into a new query string
    escaped_query_string = '&'.join(escaped_pairs)

    # Return the base URL and the escaped query string joined together
    return base_url + '?' + escaped_query_string


result:
def train_model(model, train_data, val_data, epochs, batch_size, learning_rate, loss_function, metric):
    # Define the optimizer and compile the model
    optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
    model.compile(optimizer=optimizer, loss=loss_function, metrics=[metric])

    # Define a callback to save the best model based on the metric
    checkpoint = tf.keras.callbacks.ModelCheckpoint(
        'best_model.h5', monitor=metric, save_best_only=True, mode='max')

    # Train the model on the training data and evaluate it on the validation data
    history = model.fit(
        train_data,
        epochs=epochs,
        batch_size=batch_size,
        validation_data=val_data,
        callbacks=[checkpoint]
    )

    # Return the training history
    return history


result:
def set_password(old_password, new_password):
    # Check if the old password is correct
    if old_password != 'correct_password':
        return 'Incorrect old password'

    # Set the new password
    password = new_password

    return 'Password successfully set'


result:
def split_string(string, delimiter):
    # Split the string into a list of substrings using the delimiter
    substrings = string.split(delimiter)

    # Return the list of substrings
    return substrings


result:
def remove_outliers(values, num_stds):
    # Calculate the mean and standard deviation of the values
    mean = statistics.mean(values)
    std = statistics.stdev(values)

    # Filter out any values that are more than num_stds standard deviations away from the mean
    filtered_values = [val for val in values if abs(val - mean) <= num_stds * std]

    # Return the filtered values
    return filtered_values


result:
def vcf_record_to_string(record):
    # Get the values of the attributes of the record object
    chrom = record.chrom
    pos = record.pos
    id_ = record.id
    ref = record.ref
    alt = record.alt
    qual = record.qual
    filter_ = record.filter
    info = record.info
    format_ = record.format
    sample_data = record.sample_data

    # Create a tab-delimited string of the values
    vcf_string = f"{chrom}\t{pos}\t{id_}\t{ref}\t{alt}\t{qual}\t{filter_}\t{info}\t{format_}\t{sample_data}\n"

    # Return the tab-delimited string
    return vcf_string


result:
def read_all_meter_settings(meter_ip):
    # Send a request to the meter to read all of its settings
    response = requests.get(f"http://{meter_ip}/settings")

    # Check for errors in the response
    if response.status_code != 200:
        raise ValueError("Failed to read meter settings")

    # Extract the settings from the response
    settings = response.json()

    # Return the settings
    return settings


result:
def connect_receiver(signal, receiver):
    # Connect the receiver to the signal
    signal.connect(receiver)


result:
def add_headers(request, headers):
    # Set the headers of the request
    request.headers.update(headers)


result:
def remove_tag_keep_children(tag):
    # Remove the tag but keep its children and text
    tag.extract()


result:
def convert_to_price(string_price):
    # Convert the string price to a Price object
    return Price(string_price)


result:
def get_sys_path_dirs():
    # Create a set to store the directories
    dirs = set()

    # Iterate over the elements of sys.path
    for p in sys.path:
        # Check if the element is a directory
        if os.path.isdir(p):
            # Add the directory to the set
            dirs.add(p)

    # Return the set of directories
    return dirs


result:
def get_children(obj):
    # Return the object's children
    return obj.children


result:
def store_outputs(batchable_params, outputs):
    # Iterate over the combinations of batchable parameters
    for params in batchable_params:
        # Create a unique directory name for the combination of parameters
        dir_name = "_".join(params)
        # Create the directory
        os.makedirs(dir_name, exist_ok=True)
        # Iterate over the outputs
        for output in outputs:
            # Write the output to a file in the directory
            with open(os.path.join(dir_name, "output.txt"), "w") as f:
                f.write(output)


result:
def add_events(queue, events, start_time):
    # Iterate over the events
    for event in events:
        # Check if the event occurs after the start time
        if event['time'] >= start_time:
            # Add the event to the queue
            queue.put(event)


result:
def get_next_field(string, start_pos):
    # Find the next comma separator
    comma_pos = string.find(',', start_pos)
    # Check if a comma was found
    if comma_pos != -1:
        # Return the position of the next data field and the contents of the current data field
        return comma_pos + 1, string[start_pos:comma_pos]
    else:
        # Return the position of the end of the string and the contents of the last data field
        return len(string), string[start_pos:]


result:
def add_device_to_collection(api_key, collection_id, device_id):
    # Set the endpoint URL
    url = f'https://api-m2x.att.com/v2/collections/{collection_id}/devices'
    # Set the request headers
    headers = {
        'X-M2X-KEY': api_key,
        'Content-Type': 'application/json'
    }
    # Set the request payload
    payload = {
        'id': device_id
    }
    # Send the request
    response = requests.post(url, headers=headers, json=payload)
    # Return the response
    return response


result:
def draw_image(width, height):
    # Create a new image with the specified dimensions
    image = Image.new('RGB', (width, height))
    # Get a drawing context
    draw = ImageDraw.Draw(image)
    # Draw a red rectangle
    draw.rectangle((0, 0, width, height), fill=(255, 0, 0))
    # Save the image
    image.save('image.png')


result:
def get_player_year_splits(player_id, year):
    # Read the data from a CSV file
    df = pd.read_csv('advanced_splits.csv')
    # Filter the data by player ID and year
    df = df[(df['player_id'] == player_id) & (df['year'] == year)]
    # Return the filtered data
    return df


result:
def get_client_copy(batch_client):
    # Create a copy of the batch client object
    client = copy.copy(batch_client)
    # Return the copy
    return client


result:
def recover(inputs):
    # Use the map function to apply the flat_map_nvim_io function to the inputs
    results = await asyncio.gather(*map(flat_map_nvim_io, inputs))
    # Return the results
    return results


result:
def list_command_wrapper(command):
    # Execute the command and assign the output to a variable
    output = command()
    # Return the output
    return output


result:
def print_table_rows(rows, column_sizes):
    # Iterate through the rows
    for row in rows:
        # Initialize an empty string for the row output
        row_output = ""
        # Iterate through the cells in the row
        for i, cell in enumerate(row):
            # Append the cell data to the row output string, followed by the appropriate number of spaces
            row_output += cell + " " * (column_sizes[i] - len(cell))
        # Print the row output
        print(row_output)


result:
def generate_hook_code(hook_name, *args):
    # Generate the code to call the hook with the given arguments
    code = f"{hook_name}({', '.join(map(repr, args))})"
    # Return the code
    return code


result:
def fake_on_menu_exit(world_view):
    # Call the on_menu_exit method
    world_view.on_menu_exit()
    # Set the mouse exclusivity to False
    world_view.mouse_exclusivity = False


result:
def send_sigterm(pid):
    # Send the SIGTERM signal to the process with the given pid
    os.kill(pid, signal.SIGTERM)


result:
def plot_nuclear_chart(data):
    # Get the neutron numbers and atomic numbers from the data
    neutron_numbers = [d[0] for d in data]
    atomic_numbers = [d[1] for d in data]

    # Create the plot
    fig, ax = plt.subplots()
    ax.scatter(neutron_numbers, atomic_numbers)
    ax.set_xlabel('Neutron Number (N)')
    ax.set_ylabel('Atomic Number (Z)')
    ax.set_title('Nuclear Chart')

    # Show the plot
    plt.show()


result:
def get_related_parts(part_id, reltype):
    # Connect to the database
    conn = sqlite3.connect('parts.db')
    c = conn.cursor()

    # Execute a SELECT query to get the related parts
    c.execute("SELECT related_part_id FROM relationships WHERE part_id=? AND reltype=?", (part_id, reltype))

    # Fetch the results of the query
    rows = c.fetchall()

    # Extract the related_part_id values from the rows
    related_part_ids = [row[0] for row in rows]

    # Close the connection
    conn.close()

    return related_part_ids


result:
def import_service_module(customer_id):
    # Construct the module name
    module_name = f"customer_{customer_id}.service"

    # Import the module
    service_module = importlib.import_module(module_name)

    return service_module


result:
def generate_schema(argv):
    # Create a parser for command-line arguments
    parser = argparse.ArgumentParser(description="Generate a database schema")

    # Add an optional argument for the output file
    parser.add_argument("-o", "--output", help="Output file for the generated schema")

    # Add a required argument for the input file
    parser.add_argument("input", help="Input file containing schema definitions")

    # Parse the arguments
    args = parser.parse_args(argv)

    # Open the input and output files
    with open(args.input, "r") as input_file, open(args.output, "w") as output_file:
        # Read the input file and generate the schema
        schema = generate_schema_from_file(input_file)

        # Write the generated schema to the output file
        output_file.write(schema)