code: def largest_connected_component(img, structure=None):
      r"""
      Select the largest connected binary component in an image.
  
      Treats all zero values in the input image as background and all others as foreground.
      The return value is an binary array of equal dimensions as the input array with TRUE
      values where the largest connected component is situated.
  
      Parameters
      ----------
      img : array_like
          An array containing connected objects. Will be cast to type numpy.bool_.
      structure : array_like
          A structuring element that defines the connectivity. Structure must be symmetric.
          If no structuring element is provided, one is automatically generated with a
          squared connectivity equal to one.
  
      Returns
      -------
      binary_image : ndarray
          The supplied binary image with only the largest connected component remaining.
      """
      labeled_array, num_features = label(img, structure)
      component_sizes = [
          numpy.count_nonzero(labeled_array == label_idx)
          for label_idx in range(1, num_features + 1)
      ]
      largest_component_idx = numpy.argmax(component_sizes) + 1
  
      out = numpy.zeros(img.shape, numpy.bool_)
      out[labeled_array == largest_component_idx] = True
      return out
  
imports: from operator import lt, le, gt, ge, ne, eq
  import numpy
  from scipy.ndimage import label
label_desc: Use image node_id 2 for calling a global function with example usage: largest_connected_component(img, structure) and returns: out