parameters: (self, x, name="log_prob")
docstring: `Log(P[counts])`, computed for every batch member.
  
      Args:
        x:  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 distribution
          in `self.alpha`. `x` is only legal if it sums up to one.
        name:  Name to give this Op, defaults to "log_prob".
  
      Returns:
        Log probabilities for each record, shape `[N1,...,Nm]`.
docstring_summary: `Log(P[counts])`, computed for every batch member.
docstring_tokens: Log ( P [ counts ] ) computed for every batch member .
function: def log_prob(self, x, name="log_prob"):
      """`Log(P[counts])`, computed for every batch member.
  
      Args:
        x:  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 distribution
          in `self.alpha`. `x` is only legal if it sums up to one.
        name:  Name to give this Op, defaults to "log_prob".
  
      Returns:
        Log probabilities for each record, shape `[N1,...,Nm]`.
      """
      alpha = self._alpha
      with ops.name_scope(self.name):
        with ops.op_scope([alpha, x], name):
          x = self._check_x(x)
  
          unnorm_prob = (alpha - 1) * math_ops.log(x)
          log_prob = math_ops.reduce_sum(
              unnorm_prob, reduction_indices=[-1],
              keep_dims=False) - special_math_ops.lbeta(alpha)
  
          return log_prob
function_tokens: def log_prob ( self , x , name = "log_prob" ) : alpha = self . _alpha with ops . name_scope ( self . name ) : with ops . op_scope ( [ alpha , x ] , name ) : x = self . _check_x ( x ) unnorm_prob = ( alpha - 1 ) * math_ops . log ( x ) log_prob = math_ops . reduce_sum ( unnorm_prob , reduction_indices = [ - 1 ] , keep_dims = False ) - special_math_ops . lbeta ( alpha ) return log_prob