parameters: (self, counts, name="prob")
return_statement: return super(DirichletMultinomial, self).prob(counts, name=name)
docstring: `P[counts]`, computed for every batch member.
  
      For each batch of counts `[c_1,...,c_k]`, `P[counts]` is the probability
      that after sampling `sum_j c_j` draws from this Dirichlet Multinomial
      distribution, the number of draws falling in class `j` is `c_j`.  Note that
      different sequences of draws can result in the same counts, thus the
      probability includes a combinatorial coefficient.
  
      Args:
        counts:  Non-negative tensor with dtype `dtype` and whose shape can be
          broadcast with `self.alpha`.  For fixed leading dimensions, the last
          dimension represents counts for the corresponding Dirichlet Multinomial
          distribution in `self.alpha`. `counts` is only legal if it sums up to
          `n` and its components are equal to integer values.
        name:  Name to give this Op, defaults to "prob".
  
      Returns:
        Probabilities for each record, shape `[N1,...,Nn]`.
docstring_summary: `P[counts]`, computed for every batch member.
docstring_tokens: P [ counts ] computed for every batch member .
function: def prob(self, counts, name="prob"):
      """`P[counts]`, computed for every batch member.
  
      For each batch of counts `[c_1,...,c_k]`, `P[counts]` is the probability
      that after sampling `sum_j c_j` draws from this Dirichlet Multinomial
      distribution, the number of draws falling in class `j` is `c_j`.  Note that
      different sequences of draws can result in the same counts, thus the
      probability includes a combinatorial coefficient.
  
      Args:
        counts:  Non-negative tensor with dtype `dtype` and whose shape can be
          broadcast with `self.alpha`.  For fixed leading dimensions, the last
          dimension represents counts for the corresponding Dirichlet Multinomial
          distribution in `self.alpha`. `counts` is only legal if it sums up to
          `n` and its components are equal to integer values.
        name:  Name to give this Op, defaults to "prob".
  
      Returns:
        Probabilities for each record, shape `[N1,...,Nn]`.
      """
      return super(DirichletMultinomial, self).prob(counts, name=name)
function_tokens: def prob ( self , counts , name = "prob" ) : return super ( DirichletMultinomial , self ) . prob ( counts , name = name )