class_docstr: Test cases for resource variable operators.
class_docstr_tok: ["Test","cases","for","resource","variable","operators","."]
code: def testOneWriteOneOutput(self):
      # Regression test for a bug where computations with one non-constant
      # output and one variable update were mishandled.
      for dtype in self.numeric_types:
          init = np.array([[1, 2j], [3, 4]]).astype(dtype)
          with self.session() as sess, self.test_scope():
              v = resource_variable_ops.ResourceVariable(init)
              sess.run(variables.variables_initializer([v]))
              p = array_ops.placeholder(dtype)
              x = v.assign_add(p)
              with ops.control_dependencies([x]):
                  y = v.read_value()
              self.assertAllClose(
                  np.array([[2, 1 + 2j], [4, 5]]).astype(dtype),
                  sess.run(y, {p: [[1, 1], [1, 1]]}),
              )
  
imports: import re
  import numpy
  from tensorflow.compiler.tests import xla_test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  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_state_ops
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import state_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import googletest
  from tensorflow.python.training.gradient_descent import GradientDescentOptimizer
label_desc: Use image node_id 2 for calling the VariableOpsTest obj's underlying member method code with example usage: obj.testOneWriteOneOutput() without return types