code: def fetch_dataset(
      name: str,
      version: str = "latest",
      raw: bool = False,
      kwargs_read_csv: dict = {},
      verbose: bool = True,
  ) -> pd.DataFrame:
      """
      Fetch a dataset from the skforecast-datasets repository. Available datasets
      are: 'h2o', 'items_sales', 'air_pollution', 'fuel_consumption', 'web_visits',
      'bike_sharing', 'store_item_demand'.
  
      Parameters
      ----------
      name: str
          Name of the dataset to fetch.
      version: str, int, default `'latest'`
          Version of the dataset to fetch. If 'latest', the lastest version will be
          fetched (the one in the main branch). For a list of available versions,
          see the repository branches.
      raw: bool, default `False`
          If True, the raw dataset is fetched. If False, the preprocessed dataset
          is fetched. The preprocessing consists of setting the column with the
          date/time as index and converting the index to datetime. A frequency is
          also set to the index.
      kwargs_read_csv: dict, default `{}`
          Kwargs to pass to pandas `read_csv` function.
      verbose: bool, default `True`
          If True, print information about the dataset.
  
      Returns
      -------
      df: pandas DataFrame
          Dataset.
  
      """
  
      version = "main" if version == "latest" else f"{version}"
  
      datasets = {
          "h2o": {
              "url": (
                  f"https://raw.githubusercontent.com/JoaquinAmatRodrigo/"
                  f"skforecast-datasets/{version}/data/h2o.csv"
              ),
              "sep": ",",
              "index_col": "fecha",
              "date_format": "%Y-%m-%d",
              "freq": "MS",
              "description": (
                  "Monthly expenditure ($AUD) on corticosteroid drugs that the "
                  "Australian health system had between 1991 and 2008. "
              ),
              "source": (
                  "Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice"
                  "(3rd Edition). http://pkg.robjhyndman.com/fpp3package/,"
                  "https://github.com/robjhyndman/fpp3package, http://OTexts.com/fpp3."
              ),
          },
          "h2o_exog": {
              "url": (
                  f"https://raw.githubusercontent.com/JoaquinAmatRodrigo/"
                  f"skforecast-datasets/{version}/data/h2o_exog.csv"
              ),
              "sep": ",",
              "index_col": "fecha",
              "date_format": "%Y-%m-%d",
              "freq": "MS",
              "description": (
                  "Monthly expenditure ($AUD) on corticosteroid drugs that the "
                  "Australian health system had between 1991 and 2008. Two additional "
                  "variables (exog_1, exog_2) are simulated."
              ),
              "source": (
                  "Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice "
                  "(3rd Edition). http://pkg.robjhyndman.com/fpp3package/, "
                  "https://github.com/robjhyndman/fpp3package, http://OTexts.com/fpp3."
              ),
          },
          "fuel_consumption": {
              "url": (
                  f"https://raw.githubusercontent.com/JoaquinAmatRodrigo/"
                  f"skforecast-datasets/{version}/data/consumos-combustibles-mensual.csv"
              ),
              "sep": ",",
              "index_col": "Fecha",
              "date_format": "%Y-%m-%d",
              "freq": "MS",
              "description": (
                  "Monthly fuel consumption in Spain from 1969-01-01 to 2022-08-01."
              ),
              "source": (
                  "Obtained from Corporación de Reservas Estratégicas de Productos "
                  "Petrolíferos and Corporación de Derecho Público tutelada por el "
                  "Ministerio para la Transición Ecológica y el Reto Demográfico. "
                  "https://www.cores.es/es/estadisticas"
              ),
          },
          "items_sales": {
              "url": (
                  f"https://raw.githubusercontent.com/JoaquinAmatRodrigo/"
                  f"skforecast-datasets/{version}/data/simulated_items_sales.csv"
              ),
              "sep": ",",
              "index_col": "date",
              "date_format": "%Y-%m-%d",
              "freq": "D",
              "description": "Simulated time series for the sales of 3 different items.",
              "source": "Simulated data.",
          },
          "air_quality_valencia": {
              "url": (
                  f"https://raw.githubusercontent.com/JoaquinAmatRodrigo/"
                  f"skforecast-datasets/{version}/data/air_quality_valencia.csv"
              ),
              "sep": ",",
              "index_col": "datetime",
              "date_format": "%Y-%m-%d %H:%M:%S",
              "freq": "H",
              "description": (
                  "Hourly measures of several air quimical pollutant (pm2.5, co, no, "
                  "no2, pm10, nox, o3, so2) at Valencia city."
              ),
              "source": (
                  " Red de Vigilancia y Control de la Contaminación Atmosférica, "
                  "46250054-València - Centre, "
                  "https://mediambient.gva.es/es/web/calidad-ambiental/datos-historicos."
              ),
          },
          "website_visits": {
              "url": (
                  f"https://raw.githubusercontent.com/JoaquinAmatRodrigo/"
                  f"skforecast-datasets/{version}/data/visitas_por_dia_web_cienciadedatos.csv"
              ),
              "sep": ",",
              "index_col": "date",
              "date_format": "%Y-%m-%d",
              "freq": "1D",
              "description": (
                  "Daily visits to the cienciadedatos.net website registered with the "
                  "google analytics service."
              ),
              "source": (
                  "Amat Rodrigo, J. (2021). cienciadedatos.net (1.0.0). Zenodo. "
                  "https://doi.org/10.5281/zenodo.10006330"
              ),
          },
          "bike_sharing": {
              "url": (
                  f"https://raw.githubusercontent.com/JoaquinAmatRodrigo/"
                  f"skforecast-datasets/{version}/data/bike_sharing_dataset_clean.csv"
              ),
              "sep": ",",
              "index_col": "date_time",
              "date_format": "%Y-%m-%d %H:%M:%S",
              "freq": "H",
              "description": (
                  "Hourly usage of the bike share system in the city of Washington D.C. "
                  "during the years 2011 and 2012. In addition to the number of users per "
                  "hour, information about weather conditions and holidays is available."
              ),
              "source": (
                  "Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning "
                  "Repository. https://doi.org/10.24432/C5W894."
              ),
          },
          "australia_tourism": {
              "url": (
                  f"https://raw.githubusercontent.com/JoaquinAmatRodrigo/"
                  f"skforecast-datasets/{version}/data/australia_tourism.csv"
              ),
              "sep": ",",
              "index_col": "date_time",
              "date_format": "%Y-%m-%d",
              "freq": "Q",
              "description": (
                  "Quarterly overnight trips (in thousands) from 1998 Q1 to 2016 Q4 "
                  "across Australia. The tourism regions are formed through the "
                  "aggregation of Statistical Local Areas (SLAs) which are defined by "
                  "the various State and Territory tourism authorities according to "
                  "their research and marketing needs."
              ),
              "source": (
                  "Wang, E, D Cook, and RJ Hyndman (2020). A new tidy data structure to "
                  "support exploration and modeling of temporal data, Journal of "
                  "Computational and Graphical Statistics, 29:3, 466-478, "
                  "doi:10.1080/10618600.2019.1695624."
              ),
          },
          "uk_daily_flights": {
              "url": (
                  f"https://raw.githubusercontent.com/JoaquinAmatRodrigo/"
                  f"skforecast-datasets/{version}/data/uk_daily_flights.csv"
              ),
              "sep": ",",
              "index_col": "Date",
              "date_format": "%d/%m/%Y",
              "freq": "D",
              "description": "Daily number of flights in UK from 02/01/2019 to 23/01/2022.",
              "source": (
                  "Experimental statistics published as part of the Economic activity and "
                  "social change in the UK, real-time indicators release, Published 27 "
                  "January 2022. Daily flight numbers are available in the dashboard "
                  "provided by the European Organisation for the Safety of Air Navigation "
                  "(EUROCONTROL). "
                  "https://www.ons.gov.uk/economy/economicoutputandproductivity/output/"
                  "bulletins/economicactivityandsocialchangeintheukrealtimeindicators/latest"
              ),
          },
          "wikipedia_visits": {
              "url": (
                  f"https://raw.githubusercontent.com/JoaquinAmatRodrigo/"
                  f"skforecast-datasets/{version}/data/wikipedia_visits.csv"
              ),
              "sep": ",",
              "index_col": "date",
              "date_format": "%Y-%m-%d",
              "freq": "D",
              "description": (
                  "Log daily page views for the Wikipedia page for Peyton Manning. "
                  "Scraped data using the Wikipediatrend package in R."
              ),
              "source": (
                  "https://github.com/facebook/prophet/blob/main/examples/"
                  "example_wp_log_peyton_manning.csv"
              ),
          },
          "vic_electricity": {
              "url": (
                  f"https://raw.githubusercontent.com/JoaquinAmatRodrigo/"
                  f"skforecast-datasets/{version}/data/vic_electricity.csv"
              ),
              "sep": ",",
              "index_col": "Time",
              "date_format": "%Y-%m-%dT%H:%M:%SZ",
              "freq": "30min",
              "description": "Half-hourly electricity demand for Victoria, Australia",
              "source": (
                  "O'Hara-Wild M, Hyndman R, Wang E, Godahewa R (2022).tsibbledata: Diverse "
                  "Datasets for 'tsibble'. https://tsibbledata.tidyverts.org/, "
                  "https://github.com/tidyverts/tsibbledata/. "
                  "https://tsibbledata.tidyverts.org/reference/vic_elec.html"
              ),
          },
          "store_sales": {
              "url": (
                  f"https://raw.githubusercontent.com/JoaquinAmatRodrigo/"
                  f"skforecast-datasets/{version}/data/store_sales.csv"
              ),
              "sep": ",",
              "index_col": "date",
              "date_format": "%Y-%m-%d",
              "freq": "1D",
              "description": (
                  "This dataset contains 913,000 sales transactions from 2013-01-01 to "
                  "2017-12-31 for 50 products (SKU) in 10 stores."
              ),
              "source": (
                  "The original data was obtained from: inversion. (2018). Store Item "
                  "Demand Forecasting Challenge. Kaggle. "
                  "https://kaggle.com/competitions/demand-forecasting-kernels-only"
              ),
          },
      }
  
      if name not in datasets.keys():
          raise ValueError(
              f"Dataset {name} not found. Available datasets are: {list(datasets.keys())}"
          )
  
      url = datasets[name]["url"]
  
      try:
          sep = datasets[name]["sep"]
          df = pd.read_csv(url, sep=sep, **kwargs_read_csv)
      except:
          raise ValueError(
              f"Error reading dataset {name} from {url}. Try to version = 'latest'"
          )
  
      if not raw:
          try:
              index_col = datasets[name]["index_col"]
              freq = datasets[name]["freq"]
              date_format = datasets[name]["date_format"]
              df = df.set_index(index_col)
              df.index = pd.to_datetime(df.index, format=date_format)
              df = df.asfreq(freq)
              df = df.sort_index()
          except:
              pass
  
      if verbose:
          print(name)
          print("-" * len(name))
          description = textwrap.fill(
              datasets[name]["description"], width=80
          )
          source = textwrap.fill(datasets[name]["source"], width=80)
          print(description)
          print(source)
          print(f"Shape of the dataset: {df.shape}")
  
      return df
  
label_desc: Use image node_id 1 for calling a global function with example usage: fetch_dataset(name, version, raw, kwargs_read_csv, verbose) and returns: df