code: def load_heartrate(as_series=False, dtype=DTYPE):
      """Uniform heart-rate data.
  
      A sample of heartrate data borrowed from an
      `MIT database <http://ecg.mit.edu/time-series/>`_. The sample consists
      of 150 evenly spaced (0.5 seconds) heartrate measurements.
  
      Parameters
      ----------
      as_series : bool, optional (default=False)
          Whether to return a Pandas series. 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.
  
      Returns
      -------
      rslt : array-like, shape=(n_samples,)
          The heartrate vector.
  
      Examples
      --------
      >>> from pmdarima.datasets import load_heartrate
      >>> load_heartrate()
      array([84.2697, 84.2697, 84.0619, 85.6542, 87.2093, 87.1246, 86.8726,
             86.7052, 87.5899, 89.1475, 89.8204, 89.8204, 90.4375, 91.7605,
             93.1081, 94.3291, 95.8003, 97.5119, 98.7457, 98.904 , 98.3437,
             98.3075, 98.8313, 99.0789, 98.8157, 98.2998, 97.7311, 97.6471,
             97.7922, 97.2974, 96.2042, 95.2318, 94.9367, 95.0867, 95.389 ,
             95.5414, 95.2439, 94.9415, 95.3557, 96.3423, 97.1563, 97.4026,
             96.7028, 96.5516, 97.9837, 98.9879, 97.6312, 95.4064, 93.8603,
             93.0552, 94.6012, 95.8476, 95.7692, 95.9236, 95.7692, 95.9211,
             95.8501, 94.6703, 93.0993, 91.972 , 91.7821, 91.7911, 90.807 ,
             89.3196, 88.1511, 88.7762, 90.2265, 90.8066, 91.2284, 92.4238,
             93.243 , 92.8472, 92.5926, 91.7778, 91.2974, 91.6364, 91.2952,
             91.771 , 93.2285, 93.3199, 91.8799, 91.2239, 92.4055, 93.8716,
             94.5825, 94.5594, 94.9453, 96.2412, 96.6879, 95.8295, 94.7819,
             93.4731, 92.7997, 92.963 , 92.6996, 91.9648, 91.2417, 91.9312,
             93.9548, 95.3044, 95.2511, 94.5358, 93.8093, 93.2287, 92.2065,
             92.1588, 93.6376, 94.899 , 95.1592, 95.2415, 95.5414, 95.0971,
             94.528 , 95.5887, 96.4715, 96.6158, 97.0769, 96.8531, 96.3947,
             97.4291, 98.1767, 97.0148, 96.044 , 95.9581, 96.4814, 96.5211,
             95.3629, 93.5741, 92.077 , 90.4094, 90.1751, 91.3312, 91.2883,
             89.0592, 87.052 , 86.6226, 85.7889, 85.6348, 85.3911, 83.8064,
             82.8729, 82.6266, 82.645 , 82.645 , 82.645 , 82.645 , 82.645 ,
             82.645 , 82.645 , 82.645 ])
  
      >>> load_heartrate(True).head()
      0    84.2697
      1    84.2697
      2    84.0619
      3    85.6542
      4    87.2093
      dtype: float64
  
      References
      ----------
      .. [1] Goldberger AL, Rigney DR. Nonlinear dynamics at the bedside.
             In: Glass L, Hunter P, McCulloch A, eds.
             Theory of Heart: Biomechanics, Biophysics, and Nonlinear
             Dynamics of Cardiac Function. New York: Springer-Verlag, 1991,
             pp. 583-605.
      """
      rslt = np.array(
          [
              84.2697,
              84.2697,
              84.0619,
              85.6542,
              87.2093,
              87.1246,
              86.8726,
              86.7052,
              87.5899,
              89.1475,
              89.8204,
              89.8204,
              90.4375,
              91.7605,
              93.1081,
              94.3291,
              95.8003,
              97.5119,
              98.7457,
              98.904,
              98.3437,
              98.3075,
              98.8313,
              99.0789,
              98.8157,
              98.2998,
              97.7311,
              97.6471,
              97.7922,
              97.2974,
              96.2042,
              95.2318,
              94.9367,
              95.0867,
              95.389,
              95.5414,
              95.2439,
              94.9415,
              95.3557,
              96.3423,
              97.1563,
              97.4026,
              96.7028,
              96.5516,
              97.9837,
              98.9879,
              97.6312,
              95.4064,
              93.8603,
              93.0552,
              94.6012,
              95.8476,
              95.7692,
              95.9236,
              95.7692,
              95.9211,
              95.8501,
              94.6703,
              93.0993,
              91.972,
              91.7821,
              91.7911,
              90.807,
              89.3196,
              88.1511,
              88.7762,
              90.2265,
              90.8066,
              91.2284,
              92.4238,
              93.243,
              92.8472,
              92.5926,
              91.7778,
              91.2974,
              91.6364,
              91.2952,
              91.771,
              93.2285,
              93.3199,
              91.8799,
              91.2239,
              92.4055,
              93.8716,
              94.5825,
              94.5594,
              94.9453,
              96.2412,
              96.6879,
              95.8295,
              94.7819,
              93.4731,
              92.7997,
              92.963,
              92.6996,
              91.9648,
              91.2417,
              91.9312,
              93.9548,
              95.3044,
              95.2511,
              94.5358,
              93.8093,
              93.2287,
              92.2065,
              92.1588,
              93.6376,
              94.899,
              95.1592,
              95.2415,
              95.5414,
              95.0971,
              94.528,
              95.5887,
              96.4715,
              96.6158,
              97.0769,
              96.8531,
              96.3947,
              97.4291,
              98.1767,
              97.0148,
              96.044,
              95.9581,
              96.4814,
              96.5211,
              95.3629,
              93.5741,
              92.077,
              90.4094,
              90.1751,
              91.3312,
              91.2883,
              89.0592,
              87.052,
              86.6226,
              85.7889,
              85.6348,
              85.3911,
              83.8064,
              82.8729,
              82.6266,
              82.645,
              82.645,
              82.645,
              82.645,
              82.645,
              82.645,
              82.645,
              82.645,
          ]
      ).astype(dtype)
  
      if as_series:
          return pd.Series(rslt)
      return rslt
  
label_desc: Use image node_id 1 for calling a global function with example usage: load_heartrate(as_series, dtype) and returns: rslt, pd