code: def _resolve_required_args_for_regression(**kwargs):
      """Resolves the required arguments for each issue type intended for regression tasks."""
      initial_args_dict = _REGRESSION_ARGS_DICT.copy()
      args_dict = {
          issue_type: {
              arg: kwargs.get(arg, None)
              for arg in initial_args_dict[issue_type]
          }
          for issue_type in initial_args_dict
      }
      # Some issue types have no required args.
      # This conditional lambda is used to include them in args dict.
      keep_empty_argument = lambda k: not len(_REGRESSION_ARGS_DICT[k])
  
      # Remove None values from argument list, rely on default values in IssueManager
      args_dict = {
          k: {k2: v2 for k2, v2 in v.items() if v2 is not None}
          for k, v in args_dict.items()
          if v
          or k == "label"
          or keep_empty_argument(
              k
          )  # Allow label issues to require no arguments
      }
  
      return args_dict
  
file_path: cleanlab/cleanlab/datalab/internal/issue_finder.py
imports: from __future__ import annotations
  import warnings
  from typing import TYPE_CHECKING, Any, Dict, Optional
  import numpy
  from scipy.sparse import csr_matrix
  from cleanlab.datalab.internal.issue_manager_factory import _IssueManagerFactory, list_default_issue_types
  from cleanlab.datalab.internal.model_outputs import MultiClassPredProbs, RegressionPredictions
label_desc: Use image node_id 2 for calling a global function with example usage: _resolve_required_args_for_regression() and returns: args_dict