class_docstr: Scaler for time series. Scales time series so that their span in each
  dimension is between ``min`` and ``max`` where ``value_range=(min, max)``.
  
  Parameters
  ----------
  value_range : tuple (default: (0., 1.))
      The minimum and maximum value for the output time series.
  
  Notes
  -----
      This method requires a dataset of equal-sized time series.
  
      NaNs within a time series are ignored when calculating min and max.
  
  Examples
  --------
  >>> TimeSeriesScalerMinMax(value_range=(1., 2.)).fit_transform([[0, 3, 6]])
  array([[[1. ],
          [1.5],
          [2. ]]])
  >>> TimeSeriesScalerMinMax(value_range=(1., 2.)).fit_transform(
  ...     [[numpy.nan, 3, 6]]
  ... )
  array([[[nan],
          [ 1.],
          [ 2.]]])
class_docstr_tok: ["Scaler","for","time","series",".","Scales","time","series","so","that","their","span","in","each","dimension","is","between","``","min","``","and","``","max","``","where","``","value_range=","(","min",",","max",")","``",".","Parameters","--","--","--","--","--","value_range",":","tuple","(","default",":","(","0.",",","1",".",")",")","The","minimum","and","maximum","value","for","the","output","time","series",".","Notes","--","--","-","This","method","requires","a","dataset","of","equal-sized","time","series",".","NaNs","within","a","time","series","are","ignored","when","calculating","min","and","max",".","Examples","--","--","--","--",">",">",">","TimeSeriesScalerMinMax","(","value_range=","(","1.",",","2",".",")",")",".fit_transform","(","[","[","0",",","3",",","6","]","]",")","array","(","[","[","[","1",".","]",",","[","1.5","]",",","[","2",".","]","]","]",")",">",">",">","TimeSeriesScalerMinMax","(","value_range=","(","1.",",","2",".",")",")",".fit_transform","(","...","[","[","numpy.nan",",","3",",","6","]","]","...",")","array","(","[","[","[","nan","]",",","[","1",".","]",",","[","2",".","]","]","]",")"]
docstr: Fit to data, then transform it.
  
  Parameters
  ----------
  X : array-like of shape (n_ts, sz, d)
      Time series dataset to be rescaled.
  
  Returns
  -------
  numpy.ndarray
      Resampled time series dataset.
docstr_tok: ["Fit","to","data",",","then","transform","it",".","Parameters","--","--","--","--","--","X",":","array-like","of","shape","(","n_ts",",","sz",",","d",")","Time","series","dataset","to","be","rescaled",".","Returns","--","--","--","-","numpy.ndarray","Resampled","time","series","dataset","."]
code: def fit_transform(self, X, y=None, **kwargs):
      """Fit to data, then transform it.
  
      Parameters
      ----------
      X : array-like of shape (n_ts, sz, d)
          Time series dataset to be rescaled.
  
      Returns
      -------
      numpy.ndarray
          Resampled time series dataset.
      """
      return self.fit(X).transform(X)
  
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 TimeSeriesScalerMinMax obj's underlying member method code with example usage: obj.fit_transform(X, y) and returns: self