class_docstr: Initializer that generates shapelet tensors based on a clustering of
  time series snippets.
  
  # Arguments
      dataset: a dataset of time series.
class_docstr_tok: ["Initializer","that","generates","shapelet","tensors","based","on","a","clustering","of","time","series","snippets",".","#","Arguments","dataset",":","a","dataset","of","time","series","."]
code: def get_config(self):
      return {"data": self.X_}
  
imports: from tensorflow.keras.models import Model, model_from_json
  from tensorflow.keras.layers import InputSpec, Dense, Conv1D, Layer, Input, concatenate, add
  from tensorflow.keras.metrics import categorical_accuracy, categorical_crossentropy, binary_accuracy, binary_crossentropy
  from tensorflow.keras.utils import to_categorical
  from sklearn.base import ClassifierMixin, TransformerMixin
  from sklearn.utils import check_array, check_X_y
  from sklearn.utils.validation import check_is_fitted
  from sklearn.utils.multiclass import unique_labels
  from tensorflow.keras.regularizers import l2
  from tensorflow.keras.initializers import Initializer
  import tensorflow.keras.backend
  import numpy
  import tensorflow
  import warnings
  from ..utils import to_time_series_dataset, check_dims, ts_size
  from ..bases import BaseModelPackage, TimeSeriesBaseEstimator
  from ..clustering import TimeSeriesKMeans
  from ..preprocessing import TimeSeriesScalerMinMax
label_desc: Use image node_id 3 for calling the KMeansShapeletInitializer obj's underlying member method code with example usage: obj.get_config() and returns: dict