class_docstr: Tests concurrent sessions executing on the same GPU.
code: def _run_session(self, session, results):
      n_iterations = 500
      with session as s:
          data = variables.Variable(1.0)
          with ops.device("/device:GPU:0"):
              random_seed.set_random_seed(1)
              matrix1 = variables.Variable(
                  random_ops.truncated_normal([1024, 1]), name="matrix1"
              )
              matrix2 = variables.Variable(
                  random_ops.truncated_normal([1, 1024]), name="matrix2"
              )
              x1 = math_ops.multiply(data, matrix1, name="x1")
              x3 = math_ops.matmul(
                  x1, math_ops.matmul(matrix2, matrix1)
              )
              x4 = math_ops.matmul(
                  array_ops.transpose(x3), x3, name="x4"
              )
              s.run(variables.global_variables_initializer())
  
              for _ in range(n_iterations):
                  value = s.run(x4)
                  results.add(value.flat[0])
                  if len(results) != 1:
                      break
  
imports: import itertools
  import threading
  import numpy
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import random_seed
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradient_checker_v2
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.ops.gen_array_ops import broadcast_gradient_args
  from tensorflow.python.platform import test
label_desc: Use image node_id 1 for calling the GpuMultiSessionMemoryTest obj's underlying member method code with example usage: obj._run_session(session, results) without return types