class_docstr: Tests methods specific to the Categorical distribution
code: def test_mean_and_var(self):
      torch_samples = [
          dist.Categorical(self.probs).sample().detach().cpu().numpy()
          for _ in range(self.n_samples)
      ]
      _, counts = np.unique(torch_samples, return_counts=True)
      computed_mean = float(counts[0]) / self.n_samples
      assert_equal(
          computed_mean,
          self.analytic_mean.detach().cpu().numpy()[0],
          prec=0.05,
      )
  
imports: from unittest import TestCase
  import numpy
  import pytest
  import scipy.stats
  import torch
  import pyro.distributions
  from tests.common import assert_equal
label_desc: Use image node_id 3 for calling the TestCategorical obj's underlying member method code with example usage: obj.test_mean_and_var() without return types