class_docstr: A categorical distribution over num_cats-many categories.
  
  Parameters
  ----------
  log_probs
      Tensor containing log probabilities of the individual categories, of
      shape `(*batch_shape, num_cats)`.
  F
class_docstr_tok: ["A","categorical","distribution","over","num_cats-many","categories",".","Parameters","--","--","--","--","--","log_probs","Tensor","containing","log","probabilities","of","the","individual","categories",",","of","shape","`","(","*","batch_shape",",","num_cats",")","`",".","F"]
code: def args(self) -> List:
      return [self.log_probs]
  
imports: from typing import List, Tuple
  import mxnet
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .distribution import Distribution, _sample_multiple, getF
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 11 for calling the Categorical obj's underlying member method code with example usage: obj.args() and returns: list