code: def get_anchor_from_tuple(t, state):
      # TODO: This is wrong, some of the intermediate anchors may not exist.
      anchor = {
          "feature": [],
          "mean": [],
          "precision": [],
          "coverage": [],
          "examples": [],
          "all_precision": 0,
      }
      anchor["num_preds"] = state["data"].shape[0]
      normalize_tuple = lambda x: tuple(sorted(set(x)))  # noqa
      current_t = tuple()
      for f in state["t_order"][t]:
          current_t = normalize_tuple(current_t + (f,))
  
          mean = (
              state["t_positives"][current_t]
              / state["t_nsamples"][current_t]
          )
          anchor["feature"].append(f)
          anchor["mean"].append(mean)
          anchor["precision"].append(mean)
          anchor["coverage"].append(state["t_coverage"][current_t])
          raw_idx = list(state["t_idx"][current_t])
          raw_data = state["raw_data"][raw_idx]
          covered_true = state["raw_data"][raw_idx][
              state["labels"][raw_idx] == 1
          ]
          covered_false = state["raw_data"][raw_idx][
              state["labels"][raw_idx] == 0
          ]
          exs = {}
          exs["covered"] = matrix_subset(raw_data, 10)
          exs["covered_true"] = matrix_subset(covered_true, 10)
          exs["covered_false"] = matrix_subset(covered_false, 10)
          exs["uncovered_true"] = np.array([])
          exs["uncovered_false"] = np.array([])
          anchor["examples"].append(exs)
      return anchor
  
file_path: anchor/anchor/anchor_base.py
imports: from __future__ import print_function
  import numpy
  import operator
  import copy
  import sklearn
  import collections
label_desc: Use image node_id 10 for calling the AnchorBaseBeam obj's underlying member method code with example usage: obj.get_anchor_from_tuple(t, state) and returns: anchor