parameters: (self, x, name="prob")
return_statement: return super(Beta, self).prob(x, name=name)
docstring: `P[x]`, computed for every batch member.
  
      Args:
        x:  Non-negative floating point tensor whose shape can
          be broadcast with `self.a` and `self.b`.  For fixed leading
          dimensions, the last dimension represents x for the corresponding Beta
          distribution in `self.a` and `self.b`. `x` is only legal if is
          between 0 and 1.
        name:  Name to give this Op, defaults to "pdf".
  
      Returns:
        Probabilities for each record, shape `[N1,...,Nm]`.
docstring_summary: `P[x]`, computed for every batch member.
docstring_tokens: P [ x ] computed for every batch member .
function: def prob(self, x, name="prob"):
      """`P[x]`, computed for every batch member.
  
      Args:
        x:  Non-negative floating point tensor whose shape can
          be broadcast with `self.a` and `self.b`.  For fixed leading
          dimensions, the last dimension represents x for the corresponding Beta
          distribution in `self.a` and `self.b`. `x` is only legal if is
          between 0 and 1.
        name:  Name to give this Op, defaults to "pdf".
  
      Returns:
        Probabilities for each record, shape `[N1,...,Nm]`.
      """
      return super(Beta, self).prob(x, name=name)
function_tokens: def prob ( self , x , name = "prob" ) : return super ( Beta , self ) . prob ( x , name = name )