parameters: (self, value, probs1=None)
return_statement: return self.select(comp, zeros, pmf)
docstring: r"""
          Probability mass function of Geometric distributions.
  
          Args:
              value (Tensor): A Tensor composed of only natural numbers.
              probs (Tensor): The probability of success. Default: self.probs.
  
          .. math::
              pmf(k) = probs0 ^k * probs1 if k >= 0;
              pmf(k) = 0 if k < 0.
docstring_summary: r"""
          Probability mass function of Geometric distributions.
docstring_tokens: r Probability mass function of Geometric distributions .
function: def _prob(self, value, probs1=None):
          r"""
          Probability mass function of Geometric distributions.
  
          Args:
              value (Tensor): A Tensor composed of only natural numbers.
              probs (Tensor): The probability of success. Default: self.probs.
  
          .. math::
              pmf(k) = probs0 ^k * probs1 if k >= 0;
              pmf(k) = 0 if k < 0.
          """
          value = self._check_value(value, 'value')
          value = self.cast(value, self.parameter_type)
          value = self.floor(value)
          probs1 = self._check_param_type(probs1)
          pmf = self.exp(self.log(1.0 - probs1) * value + self.log(probs1))
          zeros = self.fill(self.dtypeop(pmf), self.shape(pmf), 0.0)
          comp = self.less(value, zeros)
          return self.select(comp, zeros, pmf)
function_tokens: def _prob ( self , value , probs1 = None ) : value = self . _check_value ( value , 'value' ) value = self . cast ( value , self . parameter_type ) value = self . floor ( value ) probs1 = self . _check_param_type ( probs1 ) pmf = self . exp ( self . log ( 1.0 - probs1 ) * value + self . log ( probs1 ) ) zeros = self . fill ( self . dtypeop ( pmf ) , self . shape ( pmf ) , 0.0 ) comp = self . less ( value , zeros ) return self . select ( comp , zeros , pmf )