class_docstr: Resampler for time series. Resample time series so that they reach the
  target size.
  
  Parameters
  ----------
  sz : int
      Size of the output time series.
  
  Examples
  --------
  >>> TimeSeriesResampler(sz=5).fit_transform([[0, 3, 6]])
  array([[[0. ],
          [1.5],
          [3. ],
          [4.5],
          [6. ]]])
class_docstr_tok: ["Resampler","for","time","series",".","Resample","time","series","so","that","they","reach","the","target","size",".","Parameters","--","--","--","--","--","sz",":","int","Size","of","the","output","time","series",".","Examples","--","--","--","--",">",">",">","TimeSeriesResampler","(","sz=5",")",".fit_transform","(","[","[","0",",","3",",","6","]","]",")","array","(","[","[","[","0",".","]",",","[","1.5","]",",","[","3",".","]",",","[","4.5","]",",","[","6",".","]","]","]",")"]
code: def __init__(self, sz):
      self.sz_ = sz
  
imports: import numpy
  from sklearn.base import TransformerMixin
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from scipy.interpolate import interp1d
  import warnings
  from tslearn.utils import to_time_series_dataset, check_equal_size, ts_size, check_dims
  from tslearn.bases import TimeSeriesBaseEstimator
label_desc: Use image node_id 1 to create a new TimeSeriesResampler object from inherited base classes: TransformerMixin with example: obj = TimeSeriesResampler(sz)