code: def explain(
      data: pd.DataFrame,
      encoded_data: torch.Tensor,
      predictions: pd.DataFrame,
      target_name: str,
      target_dtype: str,
      problem_definition: ProblemDefinition,
      stat_analysis: StatisticalAnalysis,
      pred_args: PredictionArguments,
      runtime_analysis: Dict,
      explainer_blocks: Optional[List[BaseAnalysisBlock]] = [],
      ts_analysis: Optional[Dict] = {},
  ):
      """
      This procedure runs at the end of every normal `.predict()` call. Its goal is to generate prediction insights,
      potentially using information generated at the model analysis stage (e.g. confidence estimation).
  
      As in `analysis()`, any user-specified analysis blocks (see class `BaseAnalysisBlock`) are also called here.
  
      :return:
      row_insights: a DataFrame containing predictions and all generated insights at a row-level.
      """
  
      # ------------------------- #
      # Setup base insights
      # ------------------------- #
      predictions = predictions.reset_index(drop=True)
      data = data.reset_index(drop=True)
      tss = problem_definition.timeseries_settings
      row_insights = pd.DataFrame()
      global_insights = {}
  
      def _reformat_ts_columns(tss, out_df, in_df):
          if tss.is_timeseries:
              if tss.group_by:
                  for col in tss.group_by:
                      out_df[f"group_{col}"] = in_df[col]
  
              out_df[f"order_{tss.order_by}"] = in_df[tss.order_by]
              out_df[f"order_{tss.order_by}"] = get_inferred_timestamps(
                  out_df,
                  tss.order_by,
                  ts_analysis["deltas"],
                  tss,
                  stat_analysis,
                  time_format=pred_args.time_format,
              )
          return out_df
  
      if not explainer_blocks:
          predictions.rename(
              columns={"__mdb_original_index": "original_index"},
              inplace=True,
          )
          predictions = _reformat_ts_columns(tss, predictions, data)
          return predictions, global_insights
  
      row_insights["original_index"] = data["__mdb_original_index"]
      row_insights["prediction"] = predictions["prediction"]
  
      if pred_args.predict_proba:
          for col in predictions.columns:
              if "__mdb_proba" in col:
                  row_insights[col] = predictions[col]
  
      row_insights = _reformat_ts_columns(tss, row_insights, data)
  
      kwargs = {
          "data": data,
          "encoded_data": encoded_data,
          "predictions": predictions,
          "analysis": runtime_analysis,
          "target_name": target_name,
          "target_dtype": target_dtype,
          "tss": tss,
          "positive_domain": stat_analysis.positive_domain,
          "anomaly_detection": problem_definition.anomaly_detection,
          "pred_args": pred_args,
      }
  
      # ------------------------- #
      # Call explanation blocks
      # ------------------------- #
      for block in explainer_blocks:
          log.info(
              "The block %s is now running its explain() method",
              block.__class__.__name__,
          )
          row_insights, global_insights = block.explain(
              row_insights, global_insights, **kwargs
          )
  
      return row_insights, global_insights
  
file_path: lightwood/lightwood/analysis/explain.py
imports: from typing import Optional, List, Dict
  import torch
  import pandas
  from dataprep_ml import StatisticalAnalysis
  from lightwood.helpers.log import log, timed
  from lightwood.api.types import ProblemDefinition, PredictionArguments
  from lightwood.helpers.ts import get_inferred_timestamps
  from lightwood.analysis.base import BaseAnalysisBlock
label_desc: Use image node_id 1 for calling a global function with example usage: explain(data, encoded_data, predictions, target_name, target_dtype, problem_definition, stat_analysis, pred_args, runtime_analysis, explainer_blocks, ts_analysis) and returns: row_insights,  global_insights, out_df, predictions,  global_insights