code: def tsfresh_settings_wrapper(
      settings: Dict,
  ) -> List[Union[Callable, FuncWrapper]]:
      """Wrapper enabling compatibility with tsfresh feature extraction settings.
  
      [tsfresh feature extraction settings](https://tsfresh.readthedocs.io/en/latest/text/feature_extraction_settings.html)
      is how tsfresh represents a collection of features (as a dict).<br>
  
      By using this wrapper, we can plug in the features (that are present in the
      tsfresh feature extraction settings) in a tsflex ``FeatureCollection``.
      This enables to easily extract (a collection of) tsfresh features while leveraging
      the flexibility of tsflex.
  
      .. Note::
          This wrapper wraps the output of tsfresh its `MinimalFCParameters()`,
          `EfficientFCParameters()`, `IndexBasedFCParameters()`,
          `TimeBasedFCParameters()`, or `ComprehensiveFCParameters()`. <br>
          See more [here](https://github.com/blue-yonder/tsfresh/blob/main/tsfresh/feature_extraction/settings.py).
  
      Example
      -------
      ```python
      from tsflex.features import FeatureCollection, MultipleFeatureDescriptors
      from tsflex.features.integrations import tsfresh_settings_wrapper
      from tsfresh.feature_extraction import MinimalFCParameters
  
      minimal_tsfresh_feats = MultipleFeatureDescriptors(
          functions=tsfresh_settings_wrapper(MinimalFCParameters()),
          series_names=["sig_0", "sig_1"],  # list of signal names
          windows="15min", strides="2min",
      )
  
      fc = FeatureCollection(minimal_tsfresh_feats)
      fc.calculate(data)  # calculate the features on your data
      ```
  
      Parameters
      ----------
      settings: PicklableSettings
          The tsfresh base object for feature settings (which is a dict).
  
      Returns
      -------
      List[Union[Callable, FuncWrapper]]
          List of the (wrapped) tsfresh functions that are now directly compatible with
          with tsflex.
  
      """
      functions = []
      tsfresh_mod = importlib.import_module(
          "tsfresh.feature_extraction.feature_calculators"
      )
      for func_name, param in settings.items():
          func = getattr(tsfresh_mod, func_name)
          if param is None:
              functions.append(func)
          elif getattr(func, "fctype") == "combiner":
              functions.append(tsfresh_combiner_wrapper(func, param))
          else:
              for kwargs in param:
                  functions.append(
                      FuncWrapper(
                          func,
                          output_names=f"{func.__name__}_{str(kwargs)}",
                          **kwargs,
                      )
                  )
      return functions
  
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 5 for calling a global function with example usage: tsfresh_settings_wrapper(settings) and returns: functions