class_docstr: Analyzes ModelHandlerManager results.
class_docstr_tok: ["Analyzes","ModelHandlerManager","results","."]
code: def analysis(
      self, test_results: model_handler.TestResultCollection
  ) -> Tuple[DataFrame, Sequence[bool]]:
      df = extract_test_info(test_results)
      df += analyze_test_latency(
          test_results, self._use_cpu_latency_baseline
      )
      df_acc, acc_hist = analyze_test_numerics(
          test_results, self._use_cpu_numerics_baseline
      )
      df += df_acc
      # Index 0 and 1 are non trt_model results, so ignore them here.
      df_analysis_config = namedtuple(
          "df_analysis_config", "precision df_row_index"
      )
      checker_config = [
          df_analysis_config("FP32", df.n_rows - 3),
          df_analysis_config("FP16", df.n_rows - 2),
          df_analysis_config("INT8", df.n_rows - 1),
      ]
  
      checks = []
      for cc in checker_config:
          checks.append(
              self._perf_checkers[cc.precision](df, cc.df_row_index)
          )
          checks.append(
              self._acc_checkers[cc.precision](df, cc.df_row_index)
          )
  
      return df, checks, acc_hist
  
imports: import itertools
  import json
  from typing import Any, Callable, Optional, Sequence, Tuple, Union
  from collections import namedtuple
  import numpy
  from tensorflow.python.compiler.tensorrt.model_tests import model_handler
  import tensorflow.python.compiler.tensorrt.trt_convert
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 2 for calling the ResultAnalyzer obj's underlying member method code with example usage: obj.analysis(test_results) and returns: df,  checks,  acc_hist