code: def _ForUsingWhile(
      start, limit, delta, inputs, forbody, name=None, hostmem=None
  ):
      """Helper to implement a For loop using a While."""
      # To support negative delta (e.g., range(100, 0, -3)), we iterate
      # over the range(n) and use iter * delta + start as the real
      # iteration index. (e.g., for i in range(34): iter = i * (-3) +
      # 100).
      d = math_ops.abs(delta)
      # XLA on TPUs doesn't support integer division
      n = math_ops.cast(
          math_ops.cast(
              (math_ops.abs(limit - start) + d - 1), dtypes.float32
          )
          / math_ops.cast(d, dtypes.float32),
          dtypes.int32,
      )
  
      # Carried loop variables ("extra_args") are implicitly added to the input list
      # of the WhileBody function. WhileCond does not call forbody, and so does not
      # depend on any of forbody's extra_args. Since WhileCond and WhileBody
      # must have identical inputs, we have to augment the cond signature to take
      # the same types as the carried loop variables.
      body_sig = [dtypes.int32] * 4 + list(
          forbody.declared_input_types
      )[1:]
  
      cond_name = "%s_Cond" % forbody.name
  
      @function.Defun(*body_sig, func_name=cond_name)
      def WhileCond(i, n, *args):
          del args
          return i < n
  
      body_name = "%s_Body" % forbody.name
  
      @function.Defun(*body_sig, func_name=body_name)
      def WhileBody(i, n, start, delta, *args):
          """A While wrapper for forbody that handles loop-carried captured inputs."""
          for_result = forbody(start + i * delta, *args)
          # Nullary functions return an Operation. Normal functions can't do this
          # because their return values are converted to Tensors.
          if isinstance(for_result, ops.Operation):
              for_result = ()
          # Unary functions return a single Tensor value.
          elif isinstance(for_result, tensor.Tensor):
              for_result = (for_result,)
          return (i + 1, n, start, delta) + tuple(for_result)
  
      if hostmem is not None:
          hostmem = [0, 1, 2, 3] + [(4 + _) for _ in hostmem]
      else:
          hostmem = [0, 1, 2, 3]
  
      results = While(
          input_=[0, n, start, delta] + inputs,
          cond=WhileCond,
          body=WhileBody,
          name=name,
          hostmem=hostmem,
      )
      # Slice off the loop-carried captured inputs.
      return list(results[4 : len(results)])
  
imports: from tensorflow.core.framework import attr_value_pb2
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import function
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_functional_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import tensor_array_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import while_loop
  from tensorflow.python.ops.gen_functional_ops import remote_call
  from tensorflow.python.ops.gen_functional_ops import symbolic_gradient
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import dispatch
  from tensorflow.python.util import nest
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 12 for calling a global function with example usage: _ForUsingWhile(start, limit, delta, inputs, forbody, name, hostmem) and returns: list, unknown, unknown