code: def _try_handling_undefineds(
      body,
      get_state,
      set_state,
      init_vars,
      nulls,
      shape_invariants,
      symbol_names,
  ):
      """Makes a best-effort attempt to substitute undefineds with placeholders.
  
      Note: this substitution requires two things to happen:
       1. the types of loop variables could be inferred (usually by staging one
           iteration)
       2. these types could be replaced by placeholders (e.g. zero values, for
           tensors).
  
      Args:
        body: a function representing the loop body. See while_stmt.
        get_state: state getter for the loop statement. See while_stmt.
        set_state: state getter for the loop statement. See while_stmt.
        init_vars: loop variables before entering the loop. See while_stmt.
        nulls: list of boolean flags indicating whether the corresponding loop var
          is None or undefined.
        shape_invariants: user-specified shape invariant for each loop variable.
        symbol_names: list of loop variable names. See while_stmt.
  
      Returns:
        A tuple (success, new_init_vars, extra_shape_invariants, failure_message):
         * success is a boolean flag indicating
           whether types could be successfully inferred (step 1 above)
         * new_init_vars contains the loop vars, with None or undefined values
           replaced by default values, where possible (step 2 above)
         * extra_shape_invariants contains shape invariants that would be needed
           by while_stmt, for instance if the placeholder values had a shape
           different from the corresponding loop outputs
      """
      state_modified = False
      first_iter_vars = None
      failure_message = None
  
      try:
          # Stage an iteration of the loop body in a temporary graph.
          with func_graph.FuncGraph("tmp").as_default():
              # This call to set_state helps report nicer error messages when symbols
              # are inconsistently used.
              # Another complication is that non_tensor values will be autocast to
              # Tensor by while_loop, and their static value lost. So we need to account
              # that here.
              def autocast_to_tensor(v):
                  if isinstance(
                      v,
                      (
                          int,
                          float,
                          bool,
                          str,
                          list,
                          tuple,
                          np.ndarray,
                          np.generic,
                      ),
                  ):
                      init_val = tensor_conversion.convert_to_tensor_v2(
                          v
                      )
                      return array_ops.placeholder(
                          init_val.dtype, init_val.shape
                      )
                  return v
  
              autocast_init_vars = nest.map_structure(
                  autocast_to_tensor, init_vars
              )
              set_state(autocast_init_vars)
              state_modified = True
  
              body()
              first_iter_vars = get_state()
  
          # Note: the actual placeholder value doesn't matter, because as the
          # staging proved, it will be replaced by an actual value before being
          # read.
          inits_and_invariants = tuple(
              (_placeholder_value(iv, i, v) if n else (v, None))
              for v, n, iv, i in zip(
                  init_vars, nulls, first_iter_vars, shape_invariants
              )
          )
          init_vars, extra_shape_invariants = zip(*inits_and_invariants)
          success = True
  
      except (UnboundLocalError, TypeError, ValueError, KeyError):
          ag_logging.log(
              1, "Caught error while staging loop body", exc_info=True
          )
          # Fall back to the old functionality. It will likely result in an input
          # validation failure.
          exc = sys.exc_info()
          failure_message = (
              "Note: AutoGraph tried to define it automatically, but ran into a"
              " {}: {}".format(exc[0].__name__, exc[1])
          )
  
      finally:
          if state_modified:
              set_state(init_vars)
  
      # This check runs regardless, in case we captured non-Tensor inputs.
      verify_loop_init_vars(
          init_vars,
          symbol_names,
          first_iter_vars,
          extra_message=failure_message,
      )
  
      return success, init_vars, extra_shape_invariants
  
imports: import functools
  import sys
  import traceback
  import numpy
  from tensorflow.python.autograph.operators import py_builtins
  from tensorflow.python.autograph.operators import variables
  from tensorflow.python.autograph.utils import ag_logging
  from tensorflow.python.autograph.utils import misc
  from tensorflow.python.autograph.utils import tensors
  from tensorflow.python.autograph.utils import type_registry
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors_impl
  from tensorflow.python.framework import func_graph
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_conversion
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import control_flow_assert
  from tensorflow.python.ops import control_flow_util
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import tensor_array_ops
  from tensorflow.python.ops import while_loop
  from tensorflow.python.types import distribute
  from tensorflow.python.util import nest
  from tensorflow.python.util import variable_utils
label_desc: Use image node_id 21 for calling a global function with example usage: _try_handling_undefineds(body, get_state, set_state, init_vars, nulls, shape_invariants, symbol_names) and returns: success,  init_vars,  extra_shape_invariants, v, array_ops