code: def seglearn_feature_dict_wrapper(
      features_dict: Dict,
  ) -> List[FuncWrapper]:
      """Wrapper enabling compatibility with seglearn feature dictionaries.
  
      seglearn represents a collection of features as a dictionary.
  
      By using this wrapper, we can plug in the features (that are present in the
      dictionary) in a tsflex ``FeatureCollection``.
      This enables to easily extract (a collection of) seglearn features while leveraging
      the flexibility of tsflex.
  
      .. Note::
          This wrapper wraps the output of seglearn functions that return feature
          dictionaries;
          - [base_features()](https://dmbee.github.io/seglearn/feature_functions.html#seglearn.feature_functions.base_features)
          - [emg_features()](https://dmbee.github.io/seglearn/feature_functions.html#seglearn.feature_functions.emg_features)
          - [hudgins_features()](https://dmbee.github.io/seglearn/feature_functions.html#seglearn.feature_functions.hudgins_features)
          - [all_features()](https://dmbee.github.io/seglearn/feature_functions.html#seglearn.feature_functions.all_features)
  
      Example
      -------
      ```python
      from tsflex.features import FeatureCollection, MultipleFeatureDescriptors
      from tsflex.features.integrations import seglearn_feature_dict_wrapper
      from seglearn.feature_functions import base_features
  
      basic_seglearn_feats = MultipleFeatureDescriptors(
          functions=seglearn_feature_dict_wrapper(base_features()),
          series_names=["sig_0", "sig_1"],  # list of signal names
          windows="15min", strides="2min",
      )
  
      fc = FeatureCollection(basic_seglearn_feats)
      fc.calculate(data)  # calculate the features on your data
      ```
  
      Parameters
      ----------
      features_dict: Dictionary
          The seglearn collection of features (which is a dict).
  
      Returns
      -------
      List[Callable]
          List of the (wrapped) seglearn functions that are now directly compatible with
          with tsflex.
  
      """
      return [seglearn_wrapper(func) for func in features_dict.values()]
  
file_path: tsflex/tsflex/features/integrations.py
imports: import importlib
  from typing import Callable, Dict, List, Optional, Union
  import numpy
  import pandas
  from .function_wrapper import FuncWrapper, _get_name
label_desc: Use image node_id 2 for calling a global function with example usage: seglearn_feature_dict_wrapper(features_dict) and returns: unknown