class_docstr: A class that provides a way to access the submodules and parameters/buffers of a module.
  
  It provides caching mechanism to speed up submodule lookups.
  This is useful for functional programming to manipulate the module state.
class_docstr_tok: ["A","class","that","provides","a","way","to","access","the","submodules","and","parameters\/buffers","of","a","module",".","It","provides","caching","mechanism","to","speed","up","submodule","lookups",".","This","is","useful","for","functional","programming","to","manipulate","the","module","state","."]
docstr: Check that the given keys are valid.
docstr_tok: ["Check","that","the","given","keys","are","valid","."]
code: def check_keys(
      self, keys: Iterable[str]
  ) -> Tuple[List[str], List[str]]:
      """Check that the given keys are valid."""
      keys = set(keys)
      valid_keys = {
          name for name, _ in self.named_tensors(remove_duplicate=False)
      }
      missing_keys = valid_keys - keys
      unexpected_keys = keys - valid_keys
      return sorted(missing_keys), sorted(unexpected_keys)
  
imports: from typing import Dict, Iterable, List, Tuple
  import torch
label_desc: Use image node_id 14 for calling the NamedMemberAccessor obj's underlying member method code with example usage: obj.check_keys(keys) and returns: sorted,  sorted