code: def draw(G, pos=None, **kwargs):
      """
      Draw the graph G using hvPlot.
  
      Draw the graph with hvPlot with options for node positions,
      labeling, titles, and many other drawing features.
  
      Parameters
      ----------
      G : graph
         A networkx graph
      pos : dictionary, optional
         A dictionary with nodes as keys and positions as values.
         If not specified a spring layout positioning will be computed.
         See :py:mod:`networkx.drawing.layout` for functions that
         compute node positions.
      arrows : bool, optional (default=True)
         For directed graphs, if True draw arrowheads.
         Note: Arrows will be the same color as edges.
      arrowhead_length : float, optional (default=0.025)
         The length of the arrows as fraction of the overall extent of
         the graph
      with_labels :  bool, optional (default=True)
         Set to True to draw labels on the nodes.
      nodelist : list, optional (default G.nodes())
         Draw only specified nodes
      edgelist : list, optional (default=G.edges())
         Draw only specified edges
      node_size : scalar or array, optional (default=300)
         Size of nodes.  If an array is specified it must be the
         same length as nodelist.
      node_color : color string, node attribute, or array of floats, (default='r')
         Can be a single color, the name of an attribute on the nodes or
         sequence of colors with the same length as nodelist.  If the
         node_color references an attribute on the nodes or is a list of
         values they will be colormapped using the cmap and vmin, vmax
         parameters.
      node_shape :  string, optional (default='o')
         The shape of the node. Specification is as valid bokeh marker.
      alpha : float, optional (default=1.0)
         The node and edge transparency
      cmap : Colormap, optional (default=None)
         Colormap for mapping intensities of nodes
      vmin,vmax : float, optional (default=None)
         Minimum and maximum for node colormap scaling
      linewidths : [None | scalar | sequence]
         Line width of symbol border (default =1.0)
      edge_width : float, optional (default=1.0)
         Line width of edges
      edge_color : color string, or array of floats (default='r')
         Can be a single color, the name of an attribute on the edges or
         sequence of colors with the same length as the edges.  If the
         edge_color references an attribute on the edges or is a list of
         values they will be colormapped using the edge_cmap and
         edge_vmin, edge_vmax parameters.
      edge_cmap : Matplotlib colormap, optional (default=None)
         Colormap for mapping intensities of edges
      edge_vmin,edge_vmax : floats, optional (default=None)
         Minimum and maximum for edge colormap scaling
      style : string, optional (default='solid')
         Edge line style (solid|dashed|dotted,dashdot)
      labels : dictionary or string, optional (default=None)
         Node labels in a dictionary keyed by node of text labels or
         a string referencing a node attribute
      font_size : int, optional (default=12)
         Font size for text labels
      font_color : string, optional (default='black')
         Font color string
      font_family : string, optional (default='sans-serif')
         Font family
      label : string, optional
         Label for graph legend
      selection_policy : string, optional (default='nodes')
         Whether to select 'nodes', 'edges' or None on tap and selection
         events.
      inspection_policy : string, optional (default='nodes')
         Whether to select 'nodes', 'edges' or None on tap and selection
         events.
      geo : boolean, optional (default=False)
         Whether to return a GeoViews graph
      crs : cartopy.crs.CRS
         A cartopy coordinate reference system (enables a geographic plot)
      height : int, optional (default=400)
         The height of the plot in pixels
      width : int, optional (default=400)
         The width of the plot in pixels
      """
      if pos is None:
          pos = nx.drawing.spring_layout
  
      if not isinstance(pos, dict):
          pos = pos(G, **kwargs.get("layout_kwargs", {}))
  
      params, label_params = {}, {}
      label_element = Labels
      if kwargs.get("geo", False) or "crs" in kwargs:
          try:
              import geoviews
          except ImportError:
              raise ImportError(
                  "In order to use geo-related features "
                  "the geoviews library must be available. "
                  "It can be installed with pip or conda."
              )
          crs = process_crs(kwargs.get("crs"))
          label_element = geoviews.Labels
          params["cls"] = geoviews.Graph
          params["crs"] = crs
          label_params["crs"] = crs
  
      # Construct Graph object
      g = _from_networkx(G, pos, **params)
  
      if "nodelist" in kwargs:
          g.nodes.data = g.nodes.data.iloc[list(kwargs["nodelist"])]
  
      if "edgelist" in kwargs:
          edges = g.array([0, 1])
          comparisons = []
          for edge in kwargs["edgelist"]:
              comparisons.append(edges == edge)
          if len(comparisons):
              selector = np.logical_and(
                  *np.logical_or.reduce(comparisons).T
              )
              g = g.iloc[selector]
          else:
              g = g.iloc[:0]
  
      # Compute options
      inspection_policy = kwargs.pop("inspection_policy", "nodes")
      opts = dict(
          axiswise=True,
          arrowhead_length=kwargs.get("arrowhead_length", 0.025),
          directed=kwargs.pop("arrows", isinstance(G, nx.DiGraph)),
          colorbar=kwargs.pop("colorbar", False),
          padding=kwargs.get("padding", 0.1),
          width=kwargs.pop("width", 400),
          height=kwargs.pop("height", 400),
          selection_policy=kwargs.pop("selection_policy", "nodes"),
          inspection_policy=inspection_policy,
          node_fill_color="red",
      )
  
      if "_axis_defaults":
          opts.update(xaxis=None, yaxis=None, show_frame=False)
  
      opts.update(
          {
              k: kwargs.pop(k)
              for k in list(kwargs)
              if k in GraphPlot.style_opts
          }
      )
      if "node_size" in opts:
          if isinstance(opts["node_size"], str):
              opts["node_size"] = dim(opts["node_size"])
          opts["node_size"] = np.sqrt(opts["node_size"])
      if "node_color" in opts:
          opts["node_fill_color"] = opts.pop("node_color")
      if "edge_color" in opts:
          opts["edge_line_color"] = opts.pop("edge_color")
      if "node_shape" in kwargs:
          marker = kwargs.pop("node_shape")
          if marker in markers:
              marker_opts = markers[marker]
              marker = marker_opts["marker"]
              if "angle" in marker_opts:
                  Store.add_style_opts(Graph, ["node_angle"], "bokeh")
                  opts["node_angle"] = marker_opts["angle"]
          opts["node_marker"] = marker
      if "alpha" in kwargs:
          alpha = kwargs.pop("alpha")
          opts["node_alpha"] = alpha
          opts["edge_alpha"] = alpha
      if "linewidths" in kwargs:
          opts["node_line_width"] = kwargs.pop("linewidths")
      if "edge_width" in kwargs:
          opts["edge_line_width"] = kwargs.pop("edge_width")
      if "style" in kwargs:
          opts["edge_line_dash"] = kwargs.pop("style")
  
      node_styles = (
          "node_fill_color",
          "node_size",
          "node_alpha",
          "node_line_width",
      )
      for node_style in node_styles:
          if isinstance(
              opts.get(node_style), (np.ndarray, list, range)
          ):
              g = g.clone(
                  (
                      g.data,
                      g.nodes.add_dimension(
                          node_style,
                          len(g.nodes.vdims),
                          opts[node_style],
                          True,
                      ),
                  )
              )
              opts[node_style] = node_style
  
      edge_styles = (
          "edge_line_color",
          "edge_line_alpha",
          "edge_alpha",
          "edge_line_width",
      )
      for edge_style in edge_styles:
          if isinstance(
              opts.get(edge_style), (np.ndarray, list, range)
          ):
              g = g.add_dimension(
                  edge_style, len(g.vdims), opts[edge_style], True
              )
              opts[edge_style] = edge_style
  
      if opts.get("node_fill_color") in g.nodes.dimensions():
          lims = (kwargs.get("vmin", None), kwargs.get("vmax", None))
          if lims != (None, None):
              dimension = g.nodes.get_dimension(
                  opts.get("node_fill_color")
              )
              dimension.range = lims
  
      if opts.get("edge_line_color") in g.dimensions():
          lims = (
              kwargs.get("edge_vmin", None),
              kwargs.get("edge_vmax", None),
          )
          if lims != (None, None):
              dimension = g.get_dimension(opts.get("edge_line_color"))
              dimension.range = lims
  
      if inspection_policy == "nodes":
          tooltip_dims = [
              (d.label, "index_hover" if d in g.nodes.kdims else d.name)
              for d in g.nodes.kdims[2:] + g.nodes.vdims
          ]
      else:
          tooltip_dims = [
              (d.label, d.name + "_values" if d in g.kdims else d.name)
              for d in g.kdims + g.vdims
          ]
      tooltips = [
          (label, "@{%s}" % dimension_sanitizer(name))
          for label, name in tooltip_dims
          if name not in node_styles + edge_styles
      ]
      opts["tools"] = [HoverTool(tooltips=tooltips), "tap"]
  
      g.opts(**opts, backend="bokeh")
  
      # Construct Labels
      if kwargs.get("with_labels", kwargs.get("labels", False)):
          label_opts = {
              k: kwargs.pop(k)
              for k in list(kwargs)
              if k in LabelsPlot.style_opts
          }
          if "xoffset" in kwargs:
              label_opts["xoffset"] = kwargs.pop("xoffset")
          if "yoffset" in kwargs:
              label_opts["yoffset"] = kwargs.pop("yoffset")
          if "font_size" in kwargs:
              label_opts["text_font_size"] = kwargs.pop("font_size")
          if "font_color" in kwargs:
              label_opts["text_color"] = kwargs.pop("font_color")
          if "font_family" in kwargs:
              label_opts["text_font"] = kwargs.pop("font_family")
          labels = kwargs.get("labels", g.nodes.kdims[2])
          if isinstance(labels, dict):
              values = g.nodes.array(g.nodes.kdims)
              data = [
                  (x, y, labels[i])
                  for (x, y, i) in values
                  if i in labels
              ]
              labels = label_element(
                  data, g.nodes.kdims[:2], "text", **label_params
              )
          else:
              labels = label_element(
                  g.nodes, g.nodes.kdims[:2], labels, **label_params
              )
          g = g * labels.opts(**label_opts, backend="bokeh")
  
      # Apply label
      if "label" in kwargs:
          g = g.relabel(kwargs.pop("label"))
  
      # Process options
      g = _transfer_opts_cur_backend(g)
  
      return g
  
imports: from collections import defaultdict
  import numpy
  import networkx
  import holoviews
  from bokeh.models import HoverTool
  from holoviews import Graph, Labels, dim
  from holoviews.core.options import Store
  from holoviews.core.util import dimension_sanitizer
  from holoviews.plotting.bokeh import GraphPlot, LabelsPlot
  from holoviews.plotting.bokeh.styles import markers
  from .backend_transforms import _transfer_opts_cur_backend
  from .util import process_crs
  from .utilities import save, show
label_desc: Use image node_id 2 for calling a global function with example usage: draw(G, pos) and returns: g