code: def softplus(x, beta=1.0):
      """Element-wise softplus function.
  
      The softplus function is the smooth approximation of ReLU.
  
      .. math:: f(x)=\\frac{1}{\\beta}\\log(1 + \\exp(\\beta x)),
  
      where :math:`\\beta` is a parameter. The function becomes curved
      and akin to ReLU as the :math:`\\beta` is increasing.
  
      Args:
          x (:class:`~chainer.Variable` or :ref:`ndarray`):
              Input variable. A :math:`(s_1, s_2, ..., s_N)`-shaped float array.
          beta (float): Parameter :math:`\\beta`.
  
      Returns:
          ~chainer.Variable: Output variable. A
          :math:`(s_1, s_2, ..., s_N)`-shaped float array.
  
      .. admonition:: Example
  
          >>> x = np.arange(-2, 3, 2).astype(np.float32)
          >>> x
          array([-2.,  0.,  2.], dtype=float32)
          >>> F.softplus(x, beta=1.0).array
          array([0.126928 , 0.6931472, 2.126928 ], dtype=float32)
  
      """
      (y,) = Softplus(beta=beta).apply((x,))
      return y
  
imports: import numpy
  from chainer.backends import cuda
  from chainer import function_node
  import chainer.functions
  from chainer import utils
  from chainer.utils import type_check
label_desc: Use image node_id 1 for calling a global function with example usage: softplus(x, beta) and returns: y