code: def parse_net_kwargs(kwargs):
      """Parse arguments for the estimator.
  
      Resolves dotted names and instantiated classes.
  
      Examples
      --------
      >>> kwargs = {'lr': 0.1, 'module__nonlin': 'torch.nn.Hardtanh(-2, max_val=3)'}
      >>> parse_net_kwargs(kwargs)
      {'lr': 0.1, 'module__nonlin': Hardtanh(min_val=-2, max_val=3)}
  
      """
      if not kwargs:
          return kwargs
  
      resolved = {}
      for k, v in kwargs.items():
          resolved[k] = _resolve_dotted_name(v)
  
      return resolved
  
imports: from functools import partial
  from importlib import import_module
  from itertools import chain
  import re
  import shlex
  import sys
  from sklearn.base import BaseEstimator
  from sklearn.pipeline import FeatureUnion
  from sklearn.pipeline import Pipeline
label_desc: Use image node_id 6 for calling a global function with example usage: parse_net_kwargs(kwargs) and returns: resolved, kwargs