code: def snake(
      x: types.TensorLike, frequency: types.Number = 1
  ) -> tf.Tensor:
      r"""Snake activation to learn periodic functions.
  
      Computes snake activation:
  
      $$
      \mathrm{snake}(x) = \mathrm{x} + \frac{1 - \cos(2 \cdot \mathrm{frequency} \cdot x)}{2 \cdot \mathrm{frequency}}.
      $$
  
      See [Neural Networks Fail to Learn Periodic Functions and How to Fix It](https://arxiv.org/abs/2006.08195).
  
      Usage:
  
      >>> x = tf.constant([-1.0, 0.0, 1.0])
      >>> tfa.activations.snake(x)
      <tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.29192656,  0.        ,  1.7080734 ], dtype=float32)>
  
      Args:
          x: A `Tensor`.
          frequency: A scalar, frequency of the periodic part.
      Returns:
          A `Tensor`. Has the same type as `x`.
      """
      x = tf.convert_to_tensor(x)
      frequency = tf.cast(frequency, x.dtype)
  
      return x + (1 - tf.cos(2 * frequency * x)) / (2 * frequency)
  
label_desc: Use image node_id 1 for calling a global function with example usage: snake(x, frequency) and returns: unknown