class_docstr: Categorical Distribution.
  
  The probability mass function of the distribution is expressed as
  
  .. math::
      P(x = i; p) = p_i
  
  Args:
      p(:class:`~chainer.Variable` or :ref:`ndarray`): Parameter of
          distribution.
      logit(:class:`~chainer.Variable` or :ref:`ndarray`): Parameter of
          distribution representing :math:`\log\{p\} + C`. Either `p` or
          `logit` (not both) must have a value.
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code: def entropy(self):
      return -sum_mod.sum(
          chainer.distributions.utils._modified_xlogx(self.p), axis=-1
      )
  
imports: import numpy
  import chainer
  from chainer import backend
  from chainer import distribution
  from chainer.functions.activation import log_softmax
  from chainer.functions.math import exponential
  from chainer.functions.math import sum
  from chainer.utils import argument
  from chainer.utils import cache
label_desc: Use image node_id 6 for calling the Categorical obj's underlying member method code with example usage: obj.entropy() and returns: unknown