code: def load_sunspots(as_series=False, dtype=DTYPE):
      """Monthly Sunspot Numbers, 1749 - 1983
  
      Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss
      Federal Observatory, Zurich until 1960, then Tokyo Astronomical
      Observatory.
  
      Parameters
      ----------
      as_series : bool, optional (default=False)
          Whether to return a Pandas series. If True, the index will be set to
          the observed years/months. If False, will return a 1d numpy array.
  
      dtype : type, optional (default=np.float64)
          The type to return for the array. Default is np.float64, which is used
          throughout the package as the default type.
  
      Notes
      -----
      This is monthly data, so *m* should be set to 12 when using in a seasonal
      context.
  
      Examples
      --------
      >>> from pmdarima.datasets import load_sunspots
      >>> load_sunspots()
      array([58. , 62.6, 70. , ..., 55.8, 33.3, 33.4])
  
      >>> load_sunspots(True).head()
      Jan 1749    58.0
      Feb 1749    62.6
      Mar 1749    70.0
      Apr 1749    55.7
      May 1749    85.0
      dtype: float64
  
      References
      ----------
      .. [1] https://www.rdocumentation.org/packages/datasets/versions/3.6.1/topics/sunspots
  
      Returns
      -------
      rslt : array-like, shape=(n_samples,)
          The sunspots dataset. There are 2820 observations.
      """  # noqa: E501
      rslt = base._cache.get("sunspots", None)
      if rslt is None:
          data_path = join(base.get_data_path(), "sunspots.txt.gz")
          rslt = np.loadtxt(data_path).ravel()
          base._cache["sunspots"] = rslt
  
      # don't want to cache type conversion
      rslt = rslt.astype(dtype)
  
      if not as_series:
          return rslt
  
      # Otherwise we want a series and have to cleverly create the index
      index = [
          "%s %i" % (calendar.month_abbr[i + 1], year)
          for year in range(1749, 1984)
          for i in range(12)
      ]
  
      return pd.Series(rslt, index=index)
  
imports: import numpy
  import pandas
  from os.path import join
  import calendar
  from ..compat import DTYPE
  from .None import _base
label_desc: Use image node_id 1 for calling a global function with example usage: load_sunspots(as_series, dtype) and returns: pd, rslt