code: def load_indian_pines():
      """
      Loads Indian pines hyperspectral data from tensorly datasets and returns it as a bunch. This dataset could be useful for non-negative constrained decomposition methods and
      classification/segmentation applications with the available ground truth in
      http://www.ehu.eus/ccwintco/uploads/c/c4/Indian_pines_gt.mat.
      The data itself can be downloaded at "http://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat".
  
      References
      ----------
      .. [1]  Baumgardner, M. F., Biehl, L. L., Landgrebe, D. A. (2015).
              220 Band AVIRIS Hyperspectral Image Data Set:
              June 12, 1992 Indian Pine Test Site 3.
              Purdue University Research Repository.
              doi:10.4231/R7RX991C
      """
  
      path_here = dirname(__file__)
      image = np.load(path_here + "/data/Indian_pines_corrected.npy")
      reference = (
          "Baumgardner, M. F., Biehl, L. L., Landgrebe, D. A. (2015). 220 Band AVIRIS Hyperspectral "
          "Image Data Set: June 12, 1992 Indian Pine Test Site 3. Purdue University Research Repository. "
          "doi:10.4231/R7RX991C"
      )
      licence = "Licensed under Attribution 3.0 Unported (https://creativecommons.org/licenses/by/3.0/legalcode)"
      desc = (
          "Airborne Visible / Infrared Imaging Spectrometer (AVIRIS)  hyperspectral sensor data (aviris.jpl.nasa.gov/) "
          "were acquired on June 12, 1992 over the Purdue University Agronomy farm northwest "
          "of West Lafayette and the surrounding area. This scene consists of 145 times 145 pixels and 220 spectral "
          "reflectance bands in the wavelength range 0.4–2.5 10^(-6) meters."
      )
      labels = np.load(path_here + "/data/Indian_pines_gt.npy")
      wavelengths = [
          400.02,
          409.82,
          419.62,
          429.43,
          439.25,
          449.07,
          458.9,
          468.73,
          478.57,
          488.41,
          498.26,
          508.12,
          517.98,
          527.85,
          537.72,
          547.6,
          557.49,
          567.38,
          577.28,
          587.18,
          597.09,
          607.01,
          616.93,
          626.85,
          636.78,
          646.72,
          656.67,
          666.61,
          676.57,
          686.53,
          696.5,
          686.91,
          696.55,
          706.19,
          715.83,
          725.47,
          735.11,
          744.74,
          754.38,
          764.01,
          773.64,
          783.27,
          792.91,
          802.53,
          812.21,
          821.79,
          831.41,
          841.04,
          850.66,
          860.28,
          869.91,
          879.53,
          889.14,
          898.76,
          908.38,
          917.99,
          927.61,
          937.22,
          946.83,
          956.45,
          966.06,
          975.66,
          985.27,
          994.88,
          1004.48,
          1014.09,
          1023.69,
          1033.29,
          1042.89,
          1052.49,
          1062.09,
          1071.69,
          1081.29,
          1090.88,
          1100.48,
          1110.07,
          1119.66,
          1129.25,
          1138.84,
          1148.43,
          1158.02,
          1167.61,
          1177.19,
          1186.77,
          1196.36,
          1205.94,
          1215.52,
          1225.1,
          1234.68,
          1244.26,
          1253.83,
          1263.41,
          1272.98,
          1282.55,
          1273.0,
          1282.96,
          1292.93,
          1302.89,
          1312.85,
          1322.81,
          1332.77,
          1342.73,
          1352.68,
          1422.34,
          1432.28,
          1442.23,
          1452.17,
          1462.11,
          1472.05,
          1481.99,
          1491.92,
          1501.86,
          1511.79,
          1521.73,
          1531.66,
          1541.59,
          1551.52,
          1561.44,
          1571.37,
          1581.3,
          1591.22,
          1601.14,
          1611.06,
          1620.98,
          1630.9,
          1640.81,
          1650.73,
          1660.64,
          1670.56,
          1680.47,
          1690.38,
          1700.28,
          1710.19,
          1720.1,
          1730.0,
          1739.9,
          1749.81,
          1759.71,
          1769.6,
          1779.5,
          1789.4,
          1799.29,
          1809.19,
          1819.08,
          1953.26,
          1963.25,
          1973.24,
          1983.23,
          1993.22,
          2003.2,
          2013.18,
          2023.16,
          2033.13,
          2043.1,
          2053.07,
          2063.04,
          2073.0,
          2082.97,
          2092.92,
          2102.88,
          2112.83,
          2122.78,
          2132.73,
          2142.68,
          2152.62,
          2162.56,
          2172.5,
          2182.43,
          2192.37,
          2202.3,
          2260.22,
          2270.15,
          2232.07,
          2241.99,
          2251.9,
          2261.82,
          2271.73,
          2281.64,
          2291.54,
          2301.45,
          2311.35,
          2321.25,
          2331.14,
          2341.03,
          2350.92,
          2360.81,
          2370.7,
          2380.58,
          2390.46,
          2400.33,
          2410.21,
          2420.08,
          2429.95,
          2439.81,
          2449.68,
          2459.54,
          2469.4,
          2479.25,
          2489.11,
          2498.96,
      ]
  
      return Bunch(
          tensor=tl.tensor(np.array(image, "float")),
          ticks=[labels, wavelengths],
          dims=[
              "Spatial dimension",
              "Spatial dimension",
              "Hyperspectral bands",
          ],
          reference=reference,
          DESC=desc,
          LICENCE=licence,
      )
  
file_path: tensorly/tensorly/datasets/data_imports.py
imports: from os.path import dirname
  import numpy
  from urllib.request import urlopen
  import scipy.io
  from zipfile import ZipFile
  from io import BytesIO
  import tensorly
label_desc: Use image node_id 3 for calling a global function with example usage: load_indian_pines() and returns: Bunch