class_docstr: User may implement hooks for detection of overloads that are inserted by autograph
  and their implementation.
  
  In TF's AutoGraph, the AST is transformed to insert AutoGraph operators (operators/ dir),
  for example if and for statements are replaced by ag__.if_stmt and ag__.for_stmt function
  calls.
  In such operator AutoGraph checks if one of the arguments is the user-defined type of interest
  (by default a tf.Tensor of tf.data.Dataset) and if so, appropriate graph operations are
  inserted. Otherwise a _py... fallback like _py_if_stmt or _py_for_stmt is invoked, providing
  default Python semantics.
  
  The user that wants to customize AutoGraph can do so, by overloading OperatorBase.
  Any `detect_overload_x` function is used to detect the objects of user-defined type,
  corresponding `x` function is supposed to implement the particular overload.
  If `detect_overload_x` returns True, the `x` is called.
  
  For example, `detect_overload_for_stmt` can be used to recognize that the iter_ in for statement
  is a custom user-defined type and the iteration should have a custom implementation,
  that is provided in `for_stmt`.
  
  See the documentation in operators/ for description of the arguments used in those overloads,
  the names of the functions are matching, so OperatorBase.for_stmt <-> control_flow.for_stmt.
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code: def filter_(self, function, iterable):
      pass
  
label_desc: Use image node_id 47 for calling the OperatorBase obj's underlying member method code with example usage: obj.filter_(function, iterable) without return types