code: def load_covid19_serology():
      """
      Load an example dataset of COVID-19 systems serology.
      Formatted in a three-mode tensor of samples, antigens, and receptors.
  
  
      References
      ----------
      .. [1]  Tan, Z. C., Murphy, M. C., Alpay, H. S., Taylor, S. D., & Meyer, A. S. (2021).
              Tensor‐structured decomposition improves systems serology analysis.
              Molecular systems biology, 17(9), e10243.
      .. [2]  Zohar, T., Loos, C., Fischinger, S., Atyeo, C., Wang, C., Slein, M. D., ... & Alter, G. (2020).
              Compromised humoral functional evolution tracks with SARS-CoV-2 mortality. Cell, 183(6), 1508-1519.
      """
  
      path_here = dirname(__file__)
      tensor = np.load(path_here + "/data/COVID19_data.npy")
  
      sampleLabels = np.array(
          [
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Negative",
              "Mild",
              "Mild",
              "Mild",
              "Mild",
              "Mild",
              "Mild",
              "Mild",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Moderate",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Severe",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
              "Deceased",
          ],
          dtype=object,
      )
      antigenLabels = ["S", "RBD", "N", "S1", "S2", "S1 Trimer"]
      receptorLabels = [
          "IgG1",
          "IgG2",
          "IgG3",
          "IgA1",
          "IgA2",
          "IgM",
          "FcRalpha",
          "FcR2A",
          "FcR2B",
          "FcR3A",
          "FcR3B",
      ]
  
      dims = ["samples", "antigens", "receptors"]
      reference = [
          "Tan, Z. C., Murphy, M. C., Alpay, H. S., Taylor, S. D., & Meyer, A. S. (2021). Tensor‐structured decomposition improves systems serology analysis.  Molecular systems biology, 17(9), e10243.",
          "Zohar, T., Loos, C., Fischinger, S., Atyeo, C., Wang, C., Slein, M. D., ... & Alter, G. (2020). Compromised humoral functional evolution tracks with SARS-CoV-2 mortality. Cell, 183(6), 1508-1519.",
      ]
      DESC = """ A demonstration on applying CP decomposition to a COVID-19 systems serology dataset.
              Here, serum antibodies of 438 samples were systematically profiled by their binding behavior
              to SARS-CoV-2 (the virus that causes COVID-19) antigens and Fc receptors (a type of receptors 
              that expressed by immune cells) activities. Samples are labeled by the status of the patients.
              """
      LICENSE = """Copyright 2022 The Regents of the University of California
  
              Permission is hereby granted, free of charge, to any person obtaining a copy
              of this software and associated documentation files (the "Software"), to deal
              in the Software without restriction, including without limitation the rights
              to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
              copies of the Software, and to permit persons to whom the Software is
              furnished to do so, subject to the following conditions:
  
              The above copyright notice and this permission notice shall be included in all
              copies or substantial portions of the Software.
  
              THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
              IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
              FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
              AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
              LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
              OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
              SOFTWARE."""
  
      return Bunch(
          tensor=tl.tensor(tensor),
          ticks=[sampleLabels, antigenLabels, receptorLabels],
          dims=dims,
          reference=reference,
          DESC=DESC,
          LICENSE=LICENSE,
      )
  
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 2 for calling a global function with example usage: load_covid19_serology() and returns: Bunch