code: def load_tecator(split=None, return_X_y=True, return_type=None):
      """Load the Tecator time series regression problem and returns X and y.
  
      Parameters
      ----------
      split: None or one of "TRAIN", "TEST", optional (default=None)
          Whether to load the train or test instances of the problem.
          By default it loads both train and test instances (in a single container).
      return_X_y: bool, optional (default=True)
          If True, returns (features, target) separately instead of a single
          dataframe with columns for features and the target.
      return_type: valid Panel mtype str or None, optional (default=None="nested_univ")
          Memory data format specification to return X in, None = "nested_univ" type.
          str can be any supported sktime Panel mtype,
              for list of mtypes, see datatypes.MTYPE_REGISTER
              for specifications, see examples/AA_datatypes_and_datasets.ipynb
          commonly used specifications:
              "nested_univ: nested pd.DataFrame, pd.Series in cells
              "numpy3D"/"numpy3d"/"np3D": 3D np.ndarray (instance, variable, time index)
              "numpy2d"/"np2d"/"numpyflat": 2D np.ndarray (instance, time index)
              "pd-multiindex": pd.DataFrame with 2-level (instance, time) MultiIndex
          Exception is raised if the data cannot be stored in the requested type.
  
  
      Returns
      -------
      X: sktime data container, following mtype specification `return_type`
          The time series data for the problem, with n instances
      y: 1D numpy array of length n, only returned if return_X_y if True
          The target values for each time series instance in X
          If return_X_y is False, y is appended to X instead.
  
      Examples
      --------
      >>> from sktime.datasets import load_tecator
      >>> X, y = load_tecator()
  
      Notes
      -----
      Dimensionality:     univariate
      Series length:      100
      Train cases:        172
      Test cases:         43
  
      The purpose of this dataset is to measure the fat content of meat based off its near
        infrared absorbance spectrum.
      The absorbance spectrum is measured in the wavelength range of 850 nm to 1050 nm.
      The fat content is measured by standard chemical analysis methods.
      The dataset contains 215 samples of meat, each with 100 spectral measurements.
      For more information see:
      https://www.openml.org/search?type=data&sort=runs&id=505&status=active
      References
      ----------
      [1] C.Borggaard and H.H.Thodberg, "Optimal Minimal Neural Interpretation of Spectra"
      , Analytical Chemistry 64 (1992), p 545-551.
      [2] H.H.Thodberg, "Ace of Bayes: Application of Neural Networks with Pruning"
      Manuscript 1132, Danish Meat Research Institute (1993), p 1-12.
      """
      name = "Tecator"
      return _load_dataset(
          name, split, return_X_y, return_type=return_type
      )
  
imports: import os
  import zipfile
  from urllib.error import HTTPError, URLError
  from warnings import warn
  import numpy
  import pandas
  from sktime.datasets._data_io import _download_and_extract, _list_available_datasets, _load_dataset, _load_provided_dataset
  from sktime.datasets._readers_writers.tsf import load_tsf_to_dataframe
  from sktime.datasets.tsf_dataset_names import tsf_all, tsf_all_datasets
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 2 for calling a global function with example usage: load_tecator(split, return_X_y, return_type) and returns: _load_dataset