class_docstr: Stores any kind of features ready to be used by a RasaModel.
  
  Next to the input numpy array of features, it also received the number of
  dimensions of the features.
  As our features can have 1 to 4 dimensions we might have different number of numpy
  arrays stacked. The number of dimensions helps us to figure out how to handle this
  particular feature array. Also, it is automatically determined whether the feature
  array is sparse or not and the number of units is determined as well.
  
  Subclassing np.array: https://numpy.org/doc/stable/user/basics.subclassing.html
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docstr: Sets the state.
  
  Args:
      state: The state argument must be a sequence that contains the following
             elements version, shape, dtype, isFortan, rawdata.
      **kwargs: Any additional parameter
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code: def __setstate__(self, state: Any, **kwargs: Any) -> None:
      """Sets the state.
  
      Args:
          state: The state argument must be a sequence that contains the following
                 elements version, shape, dtype, isFortan, rawdata.
          **kwargs: Any additional parameter
      """
      # Needed in order to load the object
      self.number_of_dimensions = state[-3]
      self.is_sparse = state[-2]
      self.units = state[-1]
      super(FeatureArray, self).__setstate__(state[0:-3], **kwargs)
  
imports: import logging
  from typing import Optional, DefaultDict, Dict, Iterable, Text, List, Tuple, Any, Union, NamedTuple, ItemsView, overload, cast
  from collections import defaultdict, OrderedDict
  import numpy
  import scipy.sparse
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 6 for calling the FeatureArray obj's underlying member method code with example usage: obj.__setstate__(state) without return types