class_docstr: Time-series specific Support Vector Classifier.
  
  Parameters
  ----------
  C : float, optional (default=1.0)
      Penalty parameter C of the error term.
  
  kernel : string, optional (default='gak')
       Specifies the kernel type to be used in the algorithm.
       It must be one of 'gak' or a kernel accepted by ``sklearn.svm.SVC``.
       If none is given, 'gak' will be used. If a callable is given it is
       used to pre-compute the kernel matrix from data matrices; that matrix
       should be an array of shape ``(n_samples, n_samples)``.
  
  degree : int, optional (default=3)
      Degree of the polynomial kernel function ('poly').
      Ignored by all other kernels.
  
  gamma : float, optional (default='auto')
      Kernel coefficient for 'gak', 'rbf', 'poly' and 'sigmoid'.
      If gamma is 'auto' then:
  
      - for 'gak' kernel, it is computed based on a sampling of the training
        set (cf :ref:`tslearn.metrics.gamma_soft_dtw <fun-tslearn.metrics.gamma_soft_dtw>`)
      - for other kernels (eg. 'rbf'), 1/n_features will be used.
  
  coef0 : float, optional (default=0.0)
      Independent term in kernel function.
      It is only significant in 'poly' and 'sigmoid'.
  
  shrinking : boolean, optional (default=True)
      Whether to use the shrinking heuristic.
  
  probability : boolean, optional (default=False)
      Whether to enable probability estimates. This must be enabled prior
      to calling `fit`, and will slow down that method.
      Also, probability estimates are not guaranteed to match predict output.
      See our :ref:`dedicated user guide section <kernels-ml>`
      for more details.
  
  tol : float, optional (default=1e-3)
      Tolerance for stopping criterion.
  
  cache_size : float, optional (default=200.0)
      Specify the size of the kernel cache (in MB).
  
  class_weight : {dict, 'balanced'}, optional
      Set the parameter C of class i to class_weight[i]*C for
      SVC. If not given, all classes are supposed to have
      weight one.
      The "balanced" mode uses the values of y to automatically adjust
      weights inversely proportional to class frequencies in the input data
      as ``n_samples / (n_classes * np.bincount(y))``
  
  n_jobs : int or None, optional (default=None)
      The number of jobs to run in parallel for GAK cross-similarity matrix
      computations.
      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
      ``-1`` means using all processors. See scikit-learns'
      `Glossary <https://scikit-learn.org/stable/glossary.html#term-n-jobs>`_
      for more details.
  
  verbose : int, default: 0
      Enable verbose output. Note that this setting takes advantage of a
      per-process runtime setting in libsvm that, if enabled, may not work
      properly in a multithreaded context.
  
  max_iter : int, optional (default=-1)
      Hard limit on iterations within solver, or -1 for no limit.
  
  decision_function_shape : 'ovo', 'ovr', default='ovr'
      Whether to return a one-vs-rest ('ovr') decision function of shape
      (n_samples, n_classes) as all other classifiers, or the original
      one-vs-one ('ovo') decision function of libsvm which has shape
      (n_samples, n_classes * (n_classes - 1) / 2).
  
  random_state : int, RandomState instance or None, optional (default=None)
      The seed of the pseudo random number generator to use when shuffling
      the data.  If int, random_state is the seed used by the random number
      generator; If RandomState instance, random_state is the random number
      generator; If None, the random number generator is the RandomState
      instance used by `np.random`.
  
  Attributes
  ----------
  support_ : array-like, shape = [n_SV]
      Indices of support vectors.
  
  n_support_ : array-like, dtype=int32, shape = [n_class]
      Number of support vectors for each class.
      
  support_vectors_ : list of arrays of shape [n_SV, sz, d]
      List of support vectors in tslearn dataset format, one array per class
  
  dual_coef_ : array, shape = [n_class-1, n_SV]
      Coefficients of the support vector in the decision function.
      For multiclass, coefficient for all 1-vs-1 classifiers.
      The layout of the coefficients in the multiclass case is somewhat
      non-trivial. See the section about multi-class classification in the
      SVM section of the User Guide of ``sklearn`` for details.
  
  coef_ : array, shape = [n_class-1, n_features]
      Weights assigned to the features (coefficients in the primal
      problem). This is only available in the case of a linear kernel.
      `coef_` is a readonly property derived from `dual_coef_` and
      `support_vectors_`.
  
  intercept_ : array, shape = [n_class * (n_class-1) / 2]
      Constants in decision function.
  
  svm_estimator_ : sklearn.svm.SVC
      The underlying sklearn estimator
  
  Examples
  --------
  >>> from tslearn.generators import random_walk_blobs
  >>> X, y = random_walk_blobs(n_ts_per_blob=10, sz=64, d=2, n_blobs=2)
  >>> clf = TimeSeriesSVC(kernel="gak", gamma="auto", probability=True)
  >>> clf.fit(X, y).predict(X).shape
  (20,)
  >>> sv = clf.support_vectors_
  >>> len(sv)  # should be equal to the nr of classes in the clf problem
  2
  >>> sv[0].shape  # doctest: +ELLIPSIS
  (..., 64, 2)
  >>> sv_sum = sum([sv_i.shape[0] for sv_i in sv])
  >>> sv_sum == clf.svm_estimator_.n_support_.sum()
  True
  >>> clf.decision_function(X).shape
  (20,)
  >>> clf.predict_log_proba(X).shape
  (20, 2)
  >>> clf.predict_proba(X).shape
  (20, 2)
  
  References
  ----------
  Fast Global Alignment Kernels.
  Marco Cuturi.
  ICML 2011.
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code: def n_iter_(self):
      warnings.warn(
          "n_iter_ is always set to 1 for TimeSeriesSVC, since "
          "it is non-trivial to access the underlying libsvm"
      )
      return 1
  
imports: from sklearn.svm import SVC, SVR
  from sklearn.base import ClassifierMixin, RegressorMixin
  from sklearn.utils import deprecated
  from sklearn.utils import check_array, check_X_y
  from sklearn.utils.validation import check_is_fitted
  import numpy
  from ..metrics import cdist_gak, gamma_soft_dtw, VARIABLE_LENGTH_METRICS
  from ..utils import to_time_series_dataset, check_dims, to_sklearn_dataset
  from ..bases import TimeSeriesBaseEstimator
  import warnings
label_desc: Use image node_id 2 for calling the TimeSeriesSVC obj's underlying member method code with example usage: obj.n_iter_() and returns: int