code: def _load_numeric_only_dataset(
      path, row_count, column_count, sep="\t"
  ):
      # - can't use `pandas.read_csv` because it may result in 5x overhead
      # - can't use `numpy.loadtxt` because it may result in 3x overhead
      # And both mentioned above solutions are very slow compared to the one implemented below.
      dataset = np.zeros(
          (
              row_count,
              column_count,
          ),
          dtype=np.float32,
          order="F",
      )
      with open(path, "rb") as f:
          for line_idx, line in enumerate(f):
              # `str.split()` is too slow, use `numpy.fromstring()`
              row = np.fromstring(line, dtype=np.float32, sep=sep)
              assert row.size == column_count, (
                  "got too many columns at line %d (expected %d columns, got %d)"
                  % (line_idx + 1, column_count, row.size)
              )
              # doing `dataset[line_idx][:]` instead of `dataset[line_idx]` is here on purpose,
              # otherwise we may reallocate memory, while here we just copy
              dataset[line_idx][:] = row
  
      assert (
          line_idx + 1 == row_count
      ), "got too many lines (expected %d lines, got %d)" % (
          row_count,
          line_idx + 1,
      )
  
      return pd.DataFrame(dataset)
  
imports: import hashlib
  import logging
  import numpy
  import os
  import pandas
  import tarfile
  import tempfile
  import six
  import shutil
  from .core import PATH_TYPES, fspath
label_desc: Use image node_id 9 for calling a global function with example usage: _load_numeric_only_dataset(path, row_count, column_count, sep) and returns: pd