docstr: predict_links over entire graph given a threshold
  
  Parameters
  ----------
  threshold : Optional[float]
      Probability threshold. Defaults to 0.5
  anomalous : Optional[False]
      will return the edges < threshold or low confidence edges(anomaly).
  retain_old_edges : Optional[bool]
      will include old edges in predicted graph. Defaults to False.
  return_dataframe: Optional[bool]
      will return a dataframe instead of a graphistry instance. Defaults to False.
  
  Returns
  -------
  Plottable
      graphistry graph instance containing all predicted/anomalous links or dataframe
docstr_tok: ["predict_links","over","entire","graph","given","a","threshold","Parameters","--","--","--","--","--","threshold",":","Optional","[","float","]","Probability","threshold",".","Defaults","to","0.5","anomalous",":","Optional","[","False","]","will","return","the","edges","<","threshold","or","low","confidence","edges","(","anomaly",")",".","retain_old_edges",":","Optional","[","bool","]","will","include","old","edges","in","predicted","graph",".","Defaults","to","False",".","return_dataframe",":","Optional","[","bool","]","will","return","a","dataframe","instead","of","a","graphistry","instance",".","Defaults","to","False",".","Returns","--","--","--","-","Plottable","graphistry","graph","instance","containing","all","predicted\/anomalous","links","or","dataframe"]
code: def predict_links_all(
      self,
      threshold: Optional[float] = 0.5,
      anomalous: Optional[bool] = False,
      retain_old_edges: Optional[bool] = False,
      return_dataframe: Optional[bool] = False,
  ) -> Plottable:  # type: ignore
      """predict_links over entire graph given a threshold
  
      Parameters
      ----------
      threshold : Optional[float]
          Probability threshold. Defaults to 0.5
      anomalous : Optional[False]
          will return the edges < threshold or low confidence edges(anomaly).
      retain_old_edges : Optional[bool]
          will include old edges in predicted graph. Defaults to False.
      return_dataframe: Optional[bool]
          will return a dataframe instead of a graphistry instance. Defaults to False.
  
      Returns
      -------
      Plottable
          graphistry graph instance containing all predicted/anomalous links or dataframe
  
      """
      h_r = pd.DataFrame(self._triplets.numpy())  # type: ignore
      t_r = h_r.copy()
      t_r[[0, 1, 2]] = t_r[[2, 1, 0]]
  
      all_nodes = set(self._node2id.values())
  
      def fetch_triplets_for_inference(x_r):
          existing_collapsed = pd.DataFrame(
              x_r.groupby(by=[0, 1])[2].apply(set)
          ).reset_index()
  
          non_existing_collapsed = existing_collapsed[2].map(
              lambda x: set(all_nodes).difference(x)
          )
          triplets_for_inference = pd.concat(
              [existing_collapsed[[0, 1]], non_existing_collapsed],
              axis=1,
          ).explode(2)
  
          return triplets_for_inference
  
      triplets = pd.concat(
          [
              fetch_triplets_for_inference(h_r),
              fetch_triplets_for_inference(t_r),
          ],
          axis=0,
      )
      triplets = triplets[triplets[0] < triplets[2]]
      triplets = triplets.drop_duplicates().to_numpy().astype(np.int64)
  
      return self._score_triplets(
          triplets,
          threshold,
          anomalous,
          retain_old_edges,
          return_dataframe,
      )
  
imports: import logging
  import numpy
  import pandas
  from typing import Optional, Union, Callable, List, TYPE_CHECKING, Any, Tuple
  from .PlotterBase import Plottable
  from .compute.ComputeMixin import ComputeMixin
label_desc: Use image node_id 10 for calling the HeterographEmbedModuleMixin obj's underlying member method code with example usage: obj.predict_links_all(threshold, anomalous, retain_old_edges, return_dataframe) and returns: self, triplets_for_inference