class_docstr: Mixin class for all multivariate transformers in pyts.
docstr: Fit to data, then transform it.
  
  Fits transformer to `X` and `y` with optional parameters `fit_params`
  and returns a transformed version of `X`.
  
  Parameters
  ----------
  X : array-like, shape = (n_samples, n_features, n_timestamps)
      Multivariate time series.
  
  y : None or array-like, shape = (n_samples,) (default = None)
      Target values (None for unsupervised transformations).
  
  **fit_params : dict
      Additional fit parameters.
  
  Returns
  -------
  X_new : array
      Transformed array.
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code: def fit_transform(self, X, y=None, **fit_params):
      """Fit to data, then transform it.
  
      Fits transformer to `X` and `y` with optional parameters `fit_params`
      and returns a transformed version of `X`.
  
      Parameters
      ----------
      X : array-like, shape = (n_samples, n_features, n_timestamps)
          Multivariate time series.
  
      y : None or array-like, shape = (n_samples,) (default = None)
          Target values (None for unsupervised transformations).
  
      **fit_params : dict
          Additional fit parameters.
  
      Returns
      -------
      X_new : array
          Transformed array.
  
      """  # noqa: E501
      if y is None:
          # fit method of arity 1 (unsupervised transformation)
          return self.fit(X, **fit_params).transform(X)
      else:
          # fit method of arity 2 (supervised transformation)
          return self.fit(X, y, **fit_params).transform(X)
  
file_path: pyts/pyts/base.py
imports: from sklearn.metrics import accuracy_score
label_desc: Use image node_id 1 for calling the MultivariateTransformerMixin obj's underlying member method code with example usage: obj.fit_transform(X, y) and returns: self, self