code: def setUp_configure(self):
      from scipy import stats
  
      self.dist = distributions.Categorical
      self.scipy_dist = stats.multinomial
  
      self.test_targets = set(
          [
              "batch_shape",
              "event_shape",
              "entropy",
              "log_prob",
              "sample",
          ]
      )
  
      if self.logit_option:
          if self.extreme_values:
              logit = -numpy.inf * numpy.ones((3,) + self.shape).astype(
                  numpy.float32
              )
              logit[0] = 0.0
              logit = numpy.rollaxis(logit, 0, logit.ndim)
          else:
              logit = numpy.random.normal(
                  size=self.shape + (3,)
              ).astype(numpy.float32)
          p = numpy.exp(logit)
          p /= numpy.expand_dims(p.sum(axis=-1), axis=-1)
          self.params = {"logit": logit}
      else:
          if self.extreme_values:
              p = numpy.zeros((3,) + self.shape).astype(numpy.float32)
              p[0] = 1.0
              p = numpy.rollaxis(p, 0, p.ndim)
          else:
              logit = numpy.random.normal(
                  size=self.shape + (3,)
              ).astype(numpy.float32)
              p = numpy.exp(logit)
              p /= numpy.expand_dims(p.sum(axis=-1), axis=-1)
          self.params = {"p": p}
  
      n = numpy.ones(self.shape)
      self.scipy_params = {"n": n, "p": p}
  
      self.continuous = False
  
      self.old_settings = None
      if self.extreme_values:
          self.old_settings = numpy.seterr(
              divide="ignore", invalid="ignore"
          )
  
file_path: chainer/tests/chainer_tests/distributions_tests/test_categorical.py
imports: import numpy
  from chainer import cuda
  from chainer import distributions
  from chainer import testing
  from chainer.testing import array
label_desc: Use image node_id 1 for calling the TestCategorical obj's underlying member method code with example usage: obj.setUp_configure() without return types