class_docstr: A Multi-Layer Perceptron regressor for time series.
  
  This class mainly reshapes data so that it can be fed to `scikit-learn`'s
  ``MLPRegressor``.
  
  It accepts the exact same hyper-parameters as ``MLPRegressor``, check
  `scikit-learn docs <https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html>`__
  for a list of parameters and attributes.
  
  Notes
  -----
      This method requires a dataset of equal-sized time series.
  
  Examples
  --------
  >>> mlp = TimeSeriesMLPRegressor(hidden_layer_sizes=(64, 64),
  ...                               random_state=0)
  >>> mlp.fit(X=[[1, 2, 3], [1, 1.2, 3.2], [3, 2, 1]],
  ...         y=[0, 0, 1])  # doctest: +ELLIPSIS
  TimeSeriesMLPRegressor(...)
  >>> [c.shape for c in mlp.coefs_]
  [(3, 64), (64, 64), (64, 1)]
  >>> [c.shape for c in mlp.intercepts_]
  [(64,), (64,), (1,)]
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docstr: Predict the target for the provided data
  
  Parameters
  ----------
  X : array-like, shape (n_ts, sz, d)
      Test samples.
  
  Returns
  -------
  array, shape = (n_ts, ) or (n_ts, dim_y)
      Array of predicted targets
docstr_tok: ["Predict","the","target","for","the","provided","data","Parameters","--","--","--","--","--","X",":","array-like",",","shape","(","n_ts",",","sz",",","d",")","Test","samples",".","Returns","--","--","--","-","array",",","shape","=","(","n_ts",",",")","or","(","n_ts",",","dim_y",")","Array","of","predicted","targets"]
code: def predict(self, X):
      """Predict the target for the provided data
  
      Parameters
      ----------
      X : array-like, shape (n_ts, sz, d)
          Test samples.
  
      Returns
      -------
      array, shape = (n_ts, ) or (n_ts, dim_y)
          Array of predicted targets
      """
      X_ = check_array(X, force_all_finite=True, allow_nd=True)
      X_ = X_.reshape((X_.shape[0], -1))
      return super(TimeSeriesMLPRegressor, self).predict(X_)
  
file_path: tslearn/tslearn/neural_network/neural_network.py
imports: from sklearn.neural_network import MLPClassifier, MLPRegressor
  from sklearn.utils import check_array
  from ..bases import TimeSeriesBaseEstimator
label_desc: Use image node_id 2 for calling the TimeSeriesMLPRegressor obj's underlying member method code with example usage: obj.predict(X) and returns: super