code: def load_nextstrain_counts(map_location=None) -> dict:
      """
      Loads a SARS-CoV-2 dataset.
  
      The original dataset is a preprocessed intermediate ``metadata.tsv.gz`` available via
      `nextstrain <https://docs.nextstrain.org/projects/ncov/en/latest/reference/remote_inputs.html>`_.
      The ``metadata.tsv.gz`` file was then aggregated to
      (month,location,lineage) and (lineage,mutation) bins by the Broad Institute's
      `preprocessing script <https://github.com/broadinstitute/pyro-cov/blob/master/scripts/preprocess_nextstrain.py>`_.
      """
      # Download the gzipped file.
      _mkdir_p(DATA)
      basename = URL.split("/")[-1]
      gz_filename = os.path.join(DATA, basename)
      if not os.path.exists(gz_filename):
          logging.debug(f"downloading {URL}")
          urllib.request.urlretrieve(URL, gz_filename)
  
      # Decompress the file.
      filename = gz_filename.replace(".gz", "")
      if not os.path.exists(filename):
          logging.debug(f"unzipping {gz_filename}")
          subprocess.check_call(["gunzip", "-k", gz_filename])
  
      # Load tensors to the default location.
      if map_location is None:
          map_location = torch.tensor(0.0).device
      return torch.load(filename, map_location=map_location)
  
imports: import logging
  import os
  import subprocess
  import urllib
  import torch
  from .util import _mkdir_p, get_data_directory
label_desc: Use image node_id 1 for calling a global function with example usage: load_nextstrain_counts(map_location) and returns: torch