code: def run_slice(
      self,
      booster: xgb.Booster,
      dtrain: xgb.DMatrix,
      num_parallel_tree: int,
      num_classes: int,
      num_boost_round: int,
  ):
      beg = 3
      end = 7
      sliced: xgb.Booster = booster[beg:end]
      assert sliced.feature_types == booster.feature_types
  
      sliced_trees = (end - beg) * num_parallel_tree * num_classes
      assert sliced_trees == len(sliced.get_dump())
  
      sliced_trees = sliced_trees // 2
      sliced = booster[beg:end:2]
      assert sliced_trees == len(sliced.get_dump())
  
      sliced = booster[beg:...]
      sliced_trees = (
          (num_boost_round - beg) * num_parallel_tree * num_classes
      )
      assert sliced_trees == len(sliced.get_dump())
  
      sliced = booster[beg:]
      sliced_trees = (
          (num_boost_round - beg) * num_parallel_tree * num_classes
      )
      assert sliced_trees == len(sliced.get_dump())
  
      sliced = booster[:end]
      sliced_trees = end * num_parallel_tree * num_classes
      assert sliced_trees == len(sliced.get_dump())
  
      sliced = booster[...:end]
      sliced_trees = end * num_parallel_tree * num_classes
      assert sliced_trees == len(sliced.get_dump())
  
      with pytest.raises(ValueError, match=r">= 0"):
          booster[-1:0]
  
      # we do not accept empty slice.
      with pytest.raises(ValueError, match="Empty slice"):
          booster[1:1]
      # stop can not be smaller than begin
      with pytest.raises(ValueError, match=r"Invalid.*"):
          booster[3:0]
      with pytest.raises(ValueError, match=r"Invalid.*"):
          booster[3:-1]
      # negative step is not supported.
      with pytest.raises(ValueError, match=r".*>= 1.*"):
          booster[0:2:-1]
      # step can not be 0.
      with pytest.raises(ValueError, match=r".*>= 1.*"):
          booster[0:2:0]
  
      trees = [_ for _ in booster]
      assert len(trees) == num_boost_round
  
      with pytest.raises(TypeError):
          booster["wrong type"]
      with pytest.raises(IndexError):
          booster[: num_boost_round + 1]
      with pytest.raises(ValueError):
          booster[1, 2]  # too many dims
      # setitem is not implemented as model is immutable during slicing.
      with pytest.raises(TypeError):
          booster[...:end] = booster
  
      sliced_0 = booster[1:3]
      np.testing.assert_allclose(
          booster.predict(dtrain, iteration_range=(1, 3)),
          sliced_0.predict(dtrain),
      )
      sliced_1 = booster[3:7]
      np.testing.assert_allclose(
          booster.predict(dtrain, iteration_range=(3, 7)),
          sliced_1.predict(dtrain),
      )
  
      predt_0 = sliced_0.predict(dtrain, output_margin=True)
      predt_1 = sliced_1.predict(dtrain, output_margin=True)
  
      merged = predt_0 + predt_1 - 0.5  # base score.
      single = booster[1:7].predict(dtrain, output_margin=True)
      np.testing.assert_allclose(merged, single, atol=1e-6)
  
      sliced_0 = booster[1:7:2]  # 1,3,5
      sliced_1 = booster[2:8:2]  # 2,4,6
  
      predt_0 = sliced_0.predict(dtrain, output_margin=True)
      predt_1 = sliced_1.predict(dtrain, output_margin=True)
  
      merged = predt_0 + predt_1 - 0.5
      single = booster[1:7].predict(dtrain, output_margin=True)
      np.testing.assert_allclose(merged, single, atol=1e-6)
  
imports: import json
  import os
  import tempfile
  import numpy
  import pytest
  import xgboost
  from xgboost import testing
  from xgboost.testing.updater import ResetStrategy
label_desc: Use image node_id 14 for calling the TestModels obj's underlying member method code with example usage: obj.run_slice(booster, dtrain, num_parallel_tree, num_classes, num_boost_round) without return types