class_docstr: Single TDE classifier, an extension of the Bag of SFA Symbols (BOSS) model.
  
  Base classifier for the TDE classifier. Implementation of single TDE base model
  from Middlehurst (2021). [1]_
  
  Overview: input "n" series of length "m" and IndividualTDE performs a SFA
  transform to form a sparse dictionary of discretised words. The resulting
  dictionary is used with the histogram intersection distance function in a
  1-nearest neighbor.
  
  fit involves finding "n" histograms.
  
  predict uses 1 nearest neighbor with the histogram intersection distance function.
  
  Parameters
  ----------
  window_size : int, default=10
      Size of the window to use in the SFA transform.
  word_length : int, default=8
      Length of word to use to use in the SFA transform.
  norm : bool, default=False
      Whether to normalize SFA words by dropping the first Fourier coefficient.
  levels : int, default=1
      The number of spatial pyramid levels for the SFA transform.
  igb : bool, default=False
      Whether to use Information Gain Binning (IGB) or
      Multiple Coefficient Binning (MCB) for the SFA transform.
  alphabet_size : default=4
      Number of possible letters (values) for each word.
  bigrams : bool, default=False
      Whether to record word bigrams in the SFA transform.
  dim_threshold : float, default=0.85
      Accuracy threshold as a proportion of the highest accuracy dimension for words
      extracted from each dimensions. Only applicable for multivariate data.
  max_dims : int, default=20
      Maximum number of dimensions words are extracted from. Only applicable for
      multivariate data.
  typed_dict : bool, default=True
      Use a numba TypedDict to store word counts. May increase memory usage, but will
      be faster for larger datasets.
  n_jobs : int, default=1
      The number of jobs to run in parallel for both `fit` and `predict`.
      ``-1`` means using all processors.
  random_state : int or None, default=None
      Seed for random, integer.
  
  Attributes
  ----------
  n_classes_ : int
      The number of classes.
  classes_ : list
      The classes labels.
  n_instances_ : int
      The number of train cases.
  n_dims_ : int
      The number of dimensions per case.
  series_length_ : int
      The length of each series.
  
  See Also
  --------
  TemporalDictionaryEnsemble, SFA
  
  Notes
  -----
  For the Java version, see
  `TSML <https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/
  tsml/classifiers/dictionary_based/IndividualTDE.java>`_.
  
  References
  ----------
  ..  [1] Matthew Middlehurst, James Large, Gavin Cawley and Anthony Bagnall
      "The Temporal Dictionary Ensemble (TDE) Classifier for Time Series
      Classification", in proceedings of the European Conference on Machine Learning
      and Principles and Practice of Knowledge Discovery in Databases, 2020.
  
  Examples
  --------
  >>> from sktime.classification.dictionary_based import IndividualTDE
  >>> from sktime.datasets import load_unit_test
  >>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
  >>> X_test, y_test = load_unit_test(split="test", return_X_y=True) # doctest: +SKIP
  >>> clf = IndividualTDE() # doctest: +SKIP
  >>> clf.fit(X_train, y_train) # doctest: +SKIP
  IndividualTDE(...)
  >>> y_pred = clf.predict(X_test) # doctest: +SKIP
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docstr: Set current state using input pickling, required for typed Dict objects.
docstr_tok: ["Set","current","state","using","input","pickling",",","required","for","typed","Dict","objects","."]
code: def __setstate__(self, state):
      """Set current state using input pickling, required for typed Dict objects."""
      from numba import types
      from numba.typed import Dict
  
      self.__dict__.update(state)
      if self.typed_dict:
          nl = [None] * len(self._transformed_data)
          for i, pdict in enumerate(self._transformed_data):
              ndict = (
                  Dict.empty(
                      key_type=types.UniTuple(types.int64, 2),
                      value_type=types.uint32,
                  )
                  if self.levels > 1 or self.n_dims_ > 1
                  else Dict.empty(
                      key_type=types.int64, value_type=types.uint32
                  )
              )
              for key, val in pdict.items():
                  ndict[key] = val
              nl[i] = ndict
          self._transformed_data = nl
  
imports: import math
  import time
  from collections import defaultdict
  import numpy
  from joblib import Parallel, delayed
  from sklearn import preprocessing
  from sklearn.kernel_ridge import KernelRidge
  from sklearn.utils import check_random_state
  from sktime.classification.base import BaseClassifier
  from sktime.transformations.panel.dictionary_based import SFA
  from sktime.utils.validation.panel import check_X_y
  from sktime.utils.warnings import warn
label_desc: Use image node_id 3 for calling the IndividualTDE obj's underlying member method code with example usage: obj.__setstate__(state) without return types