code: def For(
      start,
      limit,
      delta,
      inputs,
      body,
      name=None,
      hostmem=None,
      rewrite_with_while=None,
  ):
      r"""out = input; for i in range(start, limit, delta) out = body(i, out).
  
      Args:
        start: A `Tensor` of type `int32`.
        limit: A `Tensor` of type `int32`.
        delta: A `Tensor` of type `int32`.
        inputs: A list of `Tensor` objects. A list of input tensors whose types are
          T.
        body: A function takes a list of tensors and returns another list of
          tensors. Both lists have the same types as (int32, T...).
        name: A name for the operation (optional).
        hostmem: A list of integer. If i is in the list, inputs[i] is a host memory
          tensor. In other words, (i+1)-th argument of the body function is
          expecting a host memory.
        rewrite_with_while: If True, using While op to implement the For.
  
      Returns:
        A list of `Tensor` objects. Has the same type as `input`.
        A list of output tensors whose types are T.
      """
      if rewrite_with_while:
          return _ForUsingWhile(
              start, limit, delta, inputs, body, name, hostmem
          )
      if body.captured_inputs:
          ret = gen_functional_ops._for(
              start,
              limit,
              delta,
              inputs + body.captured_inputs,
              _LoopBodyCaptureWrapper(body),
              name=name,
          )
          # Slice off the loop-carried captured inputs.
          ret = ret[: -len(body.captured_inputs)]
      else:
          ret = gen_functional_ops._for(
              start, limit, delta, inputs, body, name=name
          )
      if hostmem:
          num_for_params = 3  # start/limit/delta
  
          input_attr = attr_value_pb2.AttrValue()
          input_attr.list.i.extend(
              [num_for_params + i for i in hostmem]
          )
          ret[0].op._set_attr(
              "_input_hostmem", input_attr
          )  # pylint: disable=protected-access
  
          output_attr = attr_value_pb2.AttrValue()
          output_attr.list.i.extend(hostmem)
          ret[0].op._set_attr(
              "_output_hostmem", output_attr
          )  # pylint: disable=protected-access
      return ret
  
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 13 for calling a global function with example usage: For(start, limit, delta, inputs, body, name, hostmem, rewrite_with_while) and returns: ret, _ForUsingWhile