code: def merge_model_predict(
      predict: TModelPredict, predict_append: TModelPredict
  ) -> TModelPredict:
      """Append model predictions to an existing set of model predictions.
  
      TModelPredict is of the form:
          {metric_name: [mean1, mean2, ...],
          {metric_name: {metric_name: [var1, var2, ...]}})
  
      This will append the predictions
  
      Args:
          predict: Initial set of predictions.
          other_predict: Predictions to be appended.
  
      Returns:
          TModelPredict with the new predictions appended.
      """
      mu, cov = predict
      mu_append, cov_append = predict_append
      if len(mu) != len(mu_append) or len(cov) != len(cov_append):
          raise ValueError(
              "Both sets of model predictions must have the same metrics"
          )
  
      # Iterate down to the list level and simply add.
      for metric_name, metric_values in mu.items():
          mu[metric_name] = metric_values + mu_append[metric_name]
  
      for metric_name, co_cov in cov.items():
          for co_metric_name, cov_values in co_cov.items():
              cov[metric_name][co_metric_name] = (
                  cov_values + cov_append[metric_name][co_metric_name]
              )
      return mu, cov
  
file_path: Ax/ax/core/types.py
imports: import enum
  from typing import Any, Callable, DefaultDict, Dict, Hashable, List, Optional, Tuple, Union
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: merge_model_predict(predict, predict_append) and returns: mu,  cov