code: def _open_and_load(
      f, dtype, multilabel, zero_based, query_id, offset=0, length=-1
  ):
      if hasattr(f, "read"):
          (
              actual_dtype,
              data,
              ind,
              indptr,
              labels,
              query,
          ) = _load_svmlight_file(
              f, dtype, multilabel, zero_based, query_id, offset, length
          )
      else:
          with closing(_gen_open(f)) as f:
              (
                  actual_dtype,
                  data,
                  ind,
                  indptr,
                  labels,
                  query,
              ) = _load_svmlight_file(
                  f,
                  dtype,
                  multilabel,
                  zero_based,
                  query_id,
                  offset,
                  length,
              )
  
      # convert from array.array, give data the right dtype
      if not multilabel:
          labels = np.frombuffer(labels, np.float64)
      data = np.frombuffer(data, actual_dtype)
      indices = np.frombuffer(ind, np.longlong)
      indptr = np.frombuffer(indptr, dtype=np.longlong)  # never empty
      query = np.frombuffer(query, np.int64)
  
      data = np.asarray(data, dtype=dtype)  # no-op for float{32,64}
      return data, indices, indptr, labels, query
  
file_path: catboost/contrib/python/scikit-learn/py3/sklearn/datasets/_svmlight_format_io.py
imports: import os.path
  from contextlib import closing
  from numbers import Integral
  import numpy
  import scipy.sparse
  from ..None import __version__
  from ..utils import IS_PYPY, check_array
  from ..utils._param_validation import HasMethods, Interval, StrOptions, validate_params
label_desc: Use image node_id 3 for calling a global function with example usage: _open_and_load(f, dtype, multilabel, zero_based, query_id, offset, length) and returns: data,  indices,  indptr,  labels,  query