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.]]])
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docstr: Will normalize (min-max) each of the timeseries. IMPORTANT: this
  transformation is completely stateless, and is applied to each of
  the timeseries individually.
  
  Parameters
  ----------
  X : array-like of shape (n_ts, sz, d)
      Time series dataset to be rescaled.
  
  Returns
  -------
  numpy.ndarray
      Rescaled time series dataset.
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code: def transform(self, X, y=None, **kwargs):
      """Will normalize (min-max) each of the timeseries. IMPORTANT: this
      transformation is completely stateless, and is applied to each of
      the timeseries individually.
  
      Parameters
      ----------
      X : array-like of shape (n_ts, sz, d)
          Time series dataset to be rescaled.
  
      Returns
      -------
      numpy.ndarray
          Rescaled time series dataset.
      """
      value_range = self.value_range
  
      if value_range[0] >= value_range[1]:
          raise ValueError(
              "Minimum of desired range must be smaller"
              " than maximum. Got %s." % str(value_range)
          )
  
      check_is_fitted(self, "_X_fit_dims")
      X = check_array(X, allow_nd=True, force_all_finite=False)
      X_ = to_time_series_dataset(X)
      X_ = check_dims(X_, X_fit_dims=self._X_fit_dims, extend=False)
      min_t = numpy.nanmin(X_, axis=1)[:, numpy.newaxis, :]
      max_t = numpy.nanmax(X_, axis=1)[:, numpy.newaxis, :]
      range_t = max_t - min_t
      range_t[range_t == 0.0] = 1.0
      nomin = (X_ - min_t) * (value_range[1] - value_range[0])
      X_ = nomin / range_t + value_range[0]
      return 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 4 for calling the TimeSeriesScalerMinMax obj's underlying member method code with example usage: obj.transform(X, y) and returns: X_