code: def _tf_max(*args, **kwargs):
      if len(kwargs):
          kwargs_tuple = tuple(set(kwargs.keys()))
          raise ValueError(
              "These keyword arguments are "
              "currently not supported: {}".format(kwargs_tuple)
          )
      if len(args) == 1:
          rank = args[0].shape.rank
          if rank == 0:
              return args[0]
          if rank == 1:
              return math_ops.reduce_max(*args, axis=0)
          raise ValueError(
              "max(arg) currently support only tensor with rank 1, "
              "but got a tensor with rank {}".format(rank)
          )
      for arg in args:
          rank = arg.shape.rank
          if rank != 0:
              raise ValueError(
                  "max(arg1, arg2, *args) currently support "
                  "only scalar tensor, but got a tensor "
                  "with shape {}".format(rank)
              )
      return math_ops.reduce_max(args, axis=0)
  
imports: import inspect
  from tensorflow.python.autograph.utils import tensors
  from tensorflow.python.autograph.utils import type_registry
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  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 gen_parsing_ops
  from tensorflow.python.ops import gen_string_ops
  from tensorflow.python.ops import list_ops
  from tensorflow.python.ops import math_ops
label_desc: Use image node_id 28 for calling a global function with example usage: _tf_max() and returns: math_ops, args, math_ops