code: def load_matrix(
      filename: str,
      start_row: int = 0,
      count_rows: int = None,
      view: bool = False,
      dtype: typing.Optional[type] = None,
  ) -> typing.Optional[np.ndarray]:
      """Read *.ibin, *.bbib, *.hbin, *.fbin, *.dbin files with matrices.
  
      :param filename: path to the matrix file
      :param start_row: start reading vectors from this index
      :param count_rows: number of vectors to read. If None, read all vectors
      :param view: set to `True` to memory-map the file instead of loading to RAM
  
      :return: parsed matrix
      :rtype: numpy.ndarray
      """
      if dtype is None:
          dtype = guess_numpy_dtype_from_filename(filename)
          if dtype is None:
              raise Exception("Unknown file type")
      scalar_size = numpy_scalar_size(dtype)
  
      if not os.path.exists(filename):
          return None
  
      with open(filename, "rb") as f:
          rows, cols = np.fromfile(f, count=2, dtype=np.int32)
          rows = (
              (rows - start_row) if count_rows is None else count_rows
          )
          row_offset = start_row * scalar_size * cols
  
          if view:
              return np.memmap(
                  f,
                  dtype=dtype,
                  mode="r",
                  offset=8 + row_offset,
                  shape=(rows, cols),
              )
          else:
              return np.fromfile(
                  f,
                  count=rows * cols,
                  dtype=dtype,
                  offset=row_offset,
              ).reshape(rows, cols)
  
file_path: usearch/python/usearch/io.py
imports: import os
  import struct
  import typing
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: load_matrix(filename, start_row, count_rows, view, dtype) and returns: None, np, np