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 _transform_unit_sz(self, X):
      n_ts, sz, d = X.shape
      X_out = numpy.empty((n_ts, self.sz_, d))
      for i in range(X.shape[0]):
          X_out[i] = numpy.nanmean(X[i], axis=0, keepdims=True)
      return X_out
  
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 3 for calling the TimeSeriesResampler obj's underlying member method code with example usage: obj._transform_unit_sz(X) and returns: X_out