class_docstr: Create a categorical distribution object.
  
  Parameters
  ----------
  num_events : Int
      Number of events.
  prob : Tensor
      Probabilities of each event.
  logit : Tensor
      The log-odds of each event
class_docstr_tok: ["Create","a","categorical","distribution","object",".","Parameters","--","--","--","--","--","num_events",":","Int","Number","of","events",".","prob",":","Tensor","Probabilities","of","each","event",".","logit",":","Tensor","The","log-odds","of","each","event"]
docstr: Get the log probability of sampling each class.
  
  Returns
  -------
  Tensor
      Parameter tensor.
docstr_tok: ["Get","the","log","probability","of","sampling","each","class",".","Returns","--","--","--","-","Tensor","Parameter","tensor","."]
code: def logit(self):
      # pylint: disable=method-hidden
      """Get the log probability of sampling each class.
  
      Returns
      -------
      Tensor
          Parameter tensor.
      """
      return prob2logit(self.prob, False)
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/categorical.py
imports: from .distribution import Distribution
  from .utils import prob2logit, logit2prob, cached_property, sample_n_shape_converter
  from .constraint import Simplex, Real, IntegerInterval
  from ....None import np, npx
label_desc: Use image node_id 3 for calling the Categorical obj's underlying member method code with example usage: obj.logit() and returns: prob2logit