parameters: (self, x, name="cdf")
docstring: CDF of observations in `x` under these Uniform distribution(s).
  
      Args:
        x: tensor of dtype `dtype`, must be broadcastable with `a` and `b`.
        name: The name to give this op.
  
      Returns:
        cdf: tensor of dtype `dtype`, the CDFs of `x`. If `x` is `nan`, will
            return `nan`.
docstring_summary: CDF of observations in `x` under these Uniform distribution(s).
docstring_tokens: CDF of observations in x under these Uniform distribution ( s ) .
function: def cdf(self, x, name="cdf"):
      """CDF of observations in `x` under these Uniform distribution(s).
  
      Args:
        x: tensor of dtype `dtype`, must be broadcastable with `a` and `b`.
        name: The name to give this op.
  
      Returns:
        cdf: tensor of dtype `dtype`, the CDFs of `x`. If `x` is `nan`, will
            return `nan`.
      """
      with ops.name_scope(self.name):
        with ops.op_scope([self.a, self.b, x], name):
          x = ops.convert_to_tensor(x, name="x")
          if x.dtype != self.dtype:
            raise TypeError("Input x dtype does not match dtype: %s vs. %s" %
                            (x.dtype, self.dtype))
  
          broadcasted_x = x * self._ones()
          zeros = array_ops.zeros_like(x + self.a + self.b, dtype=self.dtype)
          ones = array_ops.ones_like(x + self.a + self.b, dtype=self.dtype)
          result_if_not_big = math_ops.select(
              x < self.a, zeros, (broadcasted_x - self.a) / self.range())
          return math_ops.select(x >= self.b, ones, result_if_not_big)
function_tokens: def cdf ( self , x , name = "cdf" ) : with ops . name_scope ( self . name ) : with ops . op_scope ( [ self . a , self . b , x ] , name ) : x = ops . convert_to_tensor ( x , name = "x" ) if x . dtype != self . dtype : raise TypeError ( "Input x dtype does not match dtype: %s vs. %s" % ( x . dtype , self . dtype ) ) broadcasted_x = x * self . _ones ( ) zeros = array_ops . zeros_like ( x + self . a + self . b , dtype = self . dtype ) ones = array_ops . ones_like ( x + self . a + self . b , dtype = self . dtype ) result_if_not_big = math_ops . select ( x < self . a , zeros , ( broadcasted_x - self . a ) / self . range ( ) ) return math_ops . select ( x >= self . b , ones , result_if_not_big )