code: def load_wineind(as_series=False, dtype=DTYPE):
      """Australian total wine sales by wine makers in bottles <= 1 litre.
  
      This time-series records wine sales by Australian wine makers between
      Jan 1980 -- Aug 1994. This dataset is found in the R ``forecast`` package.
  
      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_wineind
      >>> load_wineind()
      array([15136, 16733, 20016, 17708, 18019, 19227, 22893, 23739, 21133,
             22591, 26786, 29740, 15028, 17977, 20008, 21354, 19498, 22125,
             25817, 28779, 20960, 22254, 27392, 29945, 16933, 17892, 20533,
             23569, 22417, 22084, 26580, 27454, 24081, 23451, 28991, 31386,
             16896, 20045, 23471, 21747, 25621, 23859, 25500, 30998, 24475,
             23145, 29701, 34365, 17556, 22077, 25702, 22214, 26886, 23191,
             27831, 35406, 23195, 25110, 30009, 36242, 18450, 21845, 26488,
             22394, 28057, 25451, 24872, 33424, 24052, 28449, 33533, 37351,
             19969, 21701, 26249, 24493, 24603, 26485, 30723, 34569, 26689,
             26157, 32064, 38870, 21337, 19419, 23166, 28286, 24570, 24001,
             33151, 24878, 26804, 28967, 33311, 40226, 20504, 23060, 23562,
             27562, 23940, 24584, 34303, 25517, 23494, 29095, 32903, 34379,
             16991, 21109, 23740, 25552, 21752, 20294, 29009, 25500, 24166,
             26960, 31222, 38641, 14672, 17543, 25453, 32683, 22449, 22316,
             27595, 25451, 25421, 25288, 32568, 35110, 16052, 22146, 21198,
             19543, 22084, 23816, 29961, 26773, 26635, 26972, 30207, 38687,
             16974, 21697, 24179, 23757, 25013, 24019, 30345, 24488, 25156,
             25650, 30923, 37240, 17466, 19463, 24352, 26805, 25236, 24735,
             29356, 31234, 22724, 28496, 32857, 37198, 13652, 22784, 23565,
             26323, 23779, 27549, 29660, 23356])
  
      >>> load_wineind(True).head()
      Jan 1980    15136
      Feb 1980    16733
      Mar 1980    20016
      Apr 1980    17708
      May 1980    18019
      dtype: int64
  
      References
      ----------
      .. [1] https://www.rdocumentation.org/packages/forecast/versions/8.1/topics/wineind
  
      Returns
      -------
      rslt : array-like, shape=(n_samples,)
          The wineind dataset. There are 176 observations.
      """  # noqa: E501
      rslt = np.array(
          [
              15136,
              16733,
              20016,
              17708,
              18019,
              19227,
              22893,
              23739,
              21133,
              22591,
              26786,
              29740,
              15028,
              17977,
              20008,
              21354,
              19498,
              22125,
              25817,
              28779,
              20960,
              22254,
              27392,
              29945,
              16933,
              17892,
              20533,
              23569,
              22417,
              22084,
              26580,
              27454,
              24081,
              23451,
              28991,
              31386,
              16896,
              20045,
              23471,
              21747,
              25621,
              23859,
              25500,
              30998,
              24475,
              23145,
              29701,
              34365,
              17556,
              22077,
              25702,
              22214,
              26886,
              23191,
              27831,
              35406,
              23195,
              25110,
              30009,
              36242,
              18450,
              21845,
              26488,
              22394,
              28057,
              25451,
              24872,
              33424,
              24052,
              28449,
              33533,
              37351,
              19969,
              21701,
              26249,
              24493,
              24603,
              26485,
              30723,
              34569,
              26689,
              26157,
              32064,
              38870,
              21337,
              19419,
              23166,
              28286,
              24570,
              24001,
              33151,
              24878,
              26804,
              28967,
              33311,
              40226,
              20504,
              23060,
              23562,
              27562,
              23940,
              24584,
              34303,
              25517,
              23494,
              29095,
              32903,
              34379,
              16991,
              21109,
              23740,
              25552,
              21752,
              20294,
              29009,
              25500,
              24166,
              26960,
              31222,
              38641,
              14672,
              17543,
              25453,
              32683,
              22449,
              22316,
              27595,
              25451,
              25421,
              25288,
              32568,
              35110,
              16052,
              22146,
              21198,
              19543,
              22084,
              23816,
              29961,
              26773,
              26635,
              26972,
              30207,
              38687,
              16974,
              21697,
              24179,
              23757,
              25013,
              24019,
              30345,
              24488,
              25156,
              25650,
              30923,
              37240,
              17466,
              19463,
              24352,
              26805,
              25236,
              24735,
              29356,
              31234,
              22724,
              28496,
              32857,
              37198,
              13652,
              22784,
              23565,
              26323,
              23779,
              27549,
              29660,
              23356,
          ]
      ).astype(dtype)
  
      if not as_series:
          return rslt
  
      # Otherwise we want a series and have to cleverly create the index
      # (we don't want after aug in 1994, so trip Sep, Oct, Nov and Dec)
      index = [
          "%s %i" % (calendar.month_abbr[i + 1], year)
          for year in range(1980, 1995)
          for i in range(12)
      ][:-4]
  
      return pd.Series(rslt, index=index)
  
imports: import numpy
  import pandas
  import calendar
  from ..compat import DTYPE
label_desc: Use image node_id 1 for calling a global function with example usage: load_wineind(as_series, dtype) and returns: pd, rslt