code: def test_parallel_random_seed_setting():
      ctx = mx.context.current_context()
      seed_to_test = 1234
      for dtype in ["float16", "float32", "float64"]:
          # Avoid excessive test cpu runtimes
          num_temp_seeds = 25 if ctx.device_type == "gpu" else 1
          # To flush out a possible race condition, run multiple times
  
          for _ in range(20):
              # Create enough samples such that we get a meaningful distribution.
              shape = (200, 200)
              params = {"low": -1.5, "high": 3.0}
              params.update(shape=shape, dtype=dtype, ctx=ctx)
  
              # check directly
              seed = set_seed_variously(1, num_temp_seeds, seed_to_test)
              ret1 = mx.nd.random.uniform(**params)
              seed = set_seed_variously(
                  seed, num_temp_seeds, seed_to_test
              )
              ret2 = mx.nd.random.uniform(**params)
              seed = set_seed_variously(
                  seed, num_temp_seeds, seed_to_test
              )
              assert same(
                  ret1.asnumpy(), ret2.asnumpy()
              ), "ndarray seed-setting test: `uniform` should give the same result with the same seed"
  
              # check symbolic
              X = mx.sym.Variable("X")
              Y = mx.sym.random.uniform(**params) + X
              x = mx.nd.zeros(shape, dtype=dtype, ctx=ctx)
              xgrad = mx.nd.zeros(shape, dtype=dtype, ctx=ctx)
              yexec = Y._bind(ctx, {"X": x}, {"X": xgrad})
              seed = set_seed_variously(
                  seed, num_temp_seeds, seed_to_test
              )
              yexec.forward(is_train=True)
              yexec.backward(yexec.outputs[0])
              un1 = (yexec.outputs[0] - x).copyto(ctx)
              seed = set_seed_variously(
                  seed, num_temp_seeds, seed_to_test
              )
              yexec.forward()
              set_seed_variously(seed, num_temp_seeds, seed_to_test)
              un2 = (yexec.outputs[0] - x).copyto(ctx)
              assert same(
                  un1.asnumpy(), un2.asnumpy()
              ), "symbolic seed-setting test: `uniform` should give the same result with the same seed"
  
imports: import os
  import math
  import itertools
  import mxnet
  from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, assert_almost_equal
  import numpy
  import random
  from common import retry, random_seed
  import scipy.stats
  import unittest
  import pytest
  from mxnet.test_utils import *
  from mxnet.base import MXNetError
  from common import assertRaises
label_desc: Use image node_id 7 for calling a global function with example usage: test_parallel_random_seed_setting() without return types