code: def check(
      self, op, args, error, correct=None, lenient=None, strict=None
  ):
      if lenient is None:
          lenient = []
      if strict is None:
          strict = [5, 6]
  
      # Use placeholders to bypass shape inference, since only the C++
      # GraphDef level is ever scalar lenient.
      def placeholders(args, feed):
          if isinstance(args, tuple):
              return [placeholders(x, feed) for x in args]
          else:
              x = ops.convert_to_tensor(args).eval()
              fake = array_ops.placeholder(np.asarray(x).dtype)
              feed[fake] = x
              return fake
  
      # Test various GraphDef versions
      for version in strict + lenient:
          with ops.Graph().as_default() as g:
              test_util.set_producer_version(g, version)
              with self.session(graph=g) as sess:
                  feed = {}
                  xs = placeholders(args, feed)
                  x = op(*xs)
                  if version in strict:
                      with self.assertRaisesOpError(error):
                          sess.run(x, feed_dict=feed)
                  else:
                      r = sess.run(x, feed_dict=feed)
                      if correct is not None:
                          self.assertAllEqual(r, correct)
  
imports: import numpy
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_io_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import sparse_ops
  import tensorflow.python.ops.nn_grad
  from tensorflow.python.platform import test
label_desc: Use image node_id 1 for calling the ScalarTest obj's underlying member method code with example usage: obj.check(op, args, error, correct, lenient, strict) and returns: unknown, fake