code: def hinge(x, t, norm="L1", reduce="mean"):
      """Computes the hinge loss for a one-of-many classification task.
  
          .. math::
              L = \\frac{1}{N} \\sum_{n=1}^N \\sum_{k=1}^K \\left[
              \\max(0, 1 - \\delta\\{t_n = k\\} x_{nk}) \\right]^p
  
          where :math:`N` denotes the batch size and :math:`K` is the number of
          classes of interest,
  
          .. math::
              \\delta \\{ {\\rm condition} \\} = \\left \\{ \\begin{array}{cc}
              1 & {\\rm if~condition\\ is\\ true} \\\\
              -1 & {\\rm otherwise,}
              \\end{array} \\right.
  
          and
  
          .. math::
              p = \\left \\{ \\begin{array}{cc}
              1 & {\\rm if~norm} = {\\rm L1} \\\\
              2 & {\\rm if~norm} = {\\rm L2.}
              \\end{array} \\right.
  
          Let the hinge loss function :math:`l(x, \\delta)` be
          :math:`\\left[\\max(0, 1 - \\delta x) \\right]^p`.
          When :math:`x` and :math:`\\delta` have the same sign (meaning
          :math:`x` predicts the proper score for classification) and
          :math:`|x| \\geq 1`, the hinge loss :math:`l(x, \\delta) = 0`, but when
          they have opposite sign, :math:`l(x, \\delta)` increases linearly
          with :math:`x`.
  
          The output is a variable whose value depends on the value of
          the option ``reduce``. If it is ``'no'``, it holds the elementwise
          loss values. If it is ``'mean'``, it takes the mean of loss values.
  
      Args:
          x (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.
              The shape of ``x`` should be (:math:`N`, :math:`K`).
          t (:class:`~chainer.Variable` or :ref:`ndarray`):
              The :math:`N`-dimensional label vector with values
              :math:`t_n \\in \\{0, 1, 2, \\dots, K-1\\}`.
              The shape of ``t`` should be (:math:`N`,).
          norm (string): Specifies norm type. Either ``'L1'`` or ``'L2'`` is
              acceptable.
          reduce (str): Reduction option. Its value must be either
              ``'mean'`` or ``'no'``. Otherwise, :class:`ValueError` is raised.
  
      Returns:
          ~chainer.Variable:
              A variable object holding a scalar array of the
              hinge loss :math:`L`.
              If ``reduce`` is ``'no'``, the output variable holds array
              whose shape is same as one of (hence both of) input variables.
              If it is ``'mean'``, the output variable holds a scalar value.
  
      .. admonition:: Example
  
          In this case, the batch size ``N`` is 2 and the number of classes ``K``
          is 3.
  
          >>> x = np.array([[-2.0, 3.0, 0.5],
          ...               [5.0, 2.0, -0.5]]).astype(np.float32)
          >>> x
          array([[-2. ,  3. ,  0.5],
                 [ 5. ,  2. , -0.5]], dtype=float32)
          >>> t = np.array([1, 0]).astype(np.int32)
          >>> t
          array([1, 0], dtype=int32)
          >>> F.hinge(x, t)
          variable(2.5)
          >>> F.hinge(x, t, reduce='no')
          variable([[0. , 0. , 1.5],
                    [0. , 3. , 0.5]])
          >>> F.hinge(x, t, norm='L2')
          variable(5.75)
  
      """
      return Hinge(norm, reduce)(x, t)
  
file_path: chainer/chainer/functions/loss/hinge.py
imports: import numpy
  from chainer import backend
  from chainer.backends import cuda
  from chainer import function
  from chainer.utils import type_check
label_desc: Use image node_id 2 for calling a global function with example usage: hinge(x, t, norm, reduce) and returns: Hinge