class_docstr: Base class for generating random data for metric benchmarking 
class_docstr_tok: ["Base","class","for","generating","random","data","for","metric","benchmarking"]
code: def data(self):
      mx.random.seed(0)
      pred = mx.nd.random_uniform(
          0.0, 1.0, (self.n, self.c), ctx=self.pred_ctx
      )
      label = mx.nd.random_uniform(
          0.0, self.c - 1, (self.n,), ctx=self.label_ctx
      ).round()
      return label, pred
  
file_path: mxnet/benchmark/python/metric/benchmark_metric.py
imports: from __future__ import print_function
  import itertools
  import mxnet
  import sys
  import time
label_desc: Use image node_id 2 for calling the MetricDataGen obj's underlying member method code with example usage: obj.data() and returns: label,  pred