code: def _Sampler(self, num, mu, sigma, dtype, use_gpu, seed=None):
      def func():
          with self.session(use_gpu=use_gpu, graph=ops.Graph()) as sess:
              rng = random_ops.truncated_normal(
                  [num], mean=mu, stddev=sigma, dtype=dtype, seed=seed
              )
              ret = np.empty([10, num])
              for i in range(10):
                  ret[i, :] = self.evaluate(rng)
          return ret
  
      return func
  
imports: import numpy
  from tensorflow.python.eager import context
  from tensorflow.python.framework import config
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  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 gen_random_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
label_desc: Use image node_id 1 for calling the TruncatedNormalTest obj's underlying member method code with example usage: obj._Sampler(num, mu, sigma, dtype, use_gpu, seed) and returns: func, ret