code: def connect_bbox(
      bbox1,
      bbox2,
      loc1a,
      loc2a,
      loc1b,
      loc2b,
      prop_lines,
      prop_patches=None,
  ):
      if prop_patches is None:
          prop_patches = {
              **prop_lines,
              "alpha": prop_lines.get("alpha", 1) * 0.2,
              "clip_on": False,
          }
  
      c1 = BboxConnector(
          bbox1,
          bbox2,
          loc1=loc1a,
          loc2=loc2a,
          clip_on=False,
          **prop_lines,
      )
      c2 = BboxConnector(
          bbox1,
          bbox2,
          loc1=loc1b,
          loc2=loc2b,
          clip_on=False,
          **prop_lines,
      )
  
      bbox_patch1 = BboxPatch(bbox1, **prop_patches)
      bbox_patch2 = BboxPatch(bbox2, **prop_patches)
  
      p = BboxConnectorPatch(
          bbox1,
          bbox2,
          loc1a=loc1a,
          loc2a=loc2a,
          loc1b=loc1b,
          loc2b=loc2b,
          clip_on=False,
          **prop_patches,
      )
  
      return c1, c2, bbox_patch1, bbox_patch2, p
  
imports: import matplotlib.pyplot
  from matplotlib.transforms import Bbox, TransformedBbox, blended_transform_factory
  from mpl_toolkits.axes_grid1.inset_locator import BboxConnector, BboxConnectorPatch, BboxPatch
label_desc: Use image node_id 1 for calling a global function with example usage: connect_bbox(bbox1, bbox2, loc1a, loc2a, loc1b, loc2b, prop_lines, prop_patches) and returns: c1,  c2,  bbox_patch1,  bbox_patch2,  p