code: def largest_connected_component(volume):
      """Return the largest connected component of a 3D array.
  
      Parameters
      ----------
      volume : numpy.ndarray
          3D boolean array indicating a volume.
  
      Returns
      -------
      volume : numpy.ndarray
          3D boolean array with only one connected component.
  
      See Also
      --------
      nilearn.image.largest_connected_component_img : To simply operate the
          same manipulation directly on Nifti images.
  
      Notes
      -----
      **Handling big-endian in given numpy.ndarray**
      This function changes the existing byte-ordering information to new byte
      order, if the given volume has non-native data type. This operation
      is done inplace to avoid big-endian issues with scipy ndimage module.
  
      """
      if (
          hasattr(volume, "get_data")
          or hasattr(volume, "get_fdata")
          or isinstance(volume, str)
      ):
          raise ValueError(
              "Please enter a valid numpy array. For images use "
              "largest_connected_component_img."
          )
      # Get the new byteorder to handle issues like "Big-endian buffer not
      # supported on little-endian compiler" with scipy ndimage label.
      if not volume.dtype.isnative:
          volume.dtype = volume.dtype.newbyteorder("N")
  
      # We use asarray to be able to work with masked arrays.
      volume = np.asarray(volume)
      labels, label_nb = label(volume)
      if not label_nb:
          raise ValueError(
              "No non-zero values: no connected components"
          )
      if label_nb == 1:
          return volume.astype(bool)
      label_count = np.bincount(labels.ravel().astype(int))
      # discard the 0 label
      label_count[0] = 0
      return labels == label_count.argmax()
  
imports: import numpy
  from scipy.ndimage import label, maximum_filter
label_desc: Use image node_id 1 for calling a global function with example usage: largest_connected_component(volume) and returns: unknown, volume