code: def huber_loss(x, t, delta, reduce="sum_along_second_axis"):
      """Computes the Huber loss.
  
      The Huber loss is similar to the :func:`mean_squared_error` but is less
      sensitive to outliers in the data. It is defined as
  
      .. math::
  
          L_{\\delta}(a) = \\left \\{ \\begin{array}{cc}
          \\frac{1}{2} a^2 & {\\rm if~|a| \\leq \\delta} \\\\
          \\delta (|a| - \\frac{1}{2} \\delta) & {\\rm otherwise,}
          \\end{array} \\right.
  
      where :math:`a = x - t` is the difference between the input :math:`x`
      and the target :math:`t`.
  
      The loss 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 ``'sum_along_second_axis'``, loss values are
      summed up along the second axis (i.e. ``axis=1``).
  
      See: `Huber loss - Wikipedia <https://en.wikipedia.org/wiki/Huber_loss>`_.
  
      Args:
          x (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.
              The shape of ``x`` should be (:math:`N`, :math:`K`, ...) if
              ``reduce='sum_along_second_axis'``.
          t (:class:`~chainer.Variable` or :ref:`ndarray`): Target variable for
              regression. The shape of ``t`` should be
              (:math:`N`, :math:`K`, ...) if ``reduce='sum_along_second_axis'``.
          delta (float): Constant variable for Huber loss function
              as used in definition.
          reduce (str): Reduction option. Its value must be either
              ``'sum_along_second_axis'`` or ``'no'``. Otherwise,
              :class:`ValueError` is raised.
  
      Returns:
          ~chainer.Variable:
              A variable object holding a scalar array of the
              Huber loss :math:`L_{\\delta}`.
              If ``reduce`` is ``'no'``, the output variable holds array
              whose shape is same as one of (hence both of) input variables.
              If it is ``'sum_along_second_axis'``, the shape of the array
              is same as the input variables, except the second axis is removed.
  
      .. admonition:: Example
  
          Example without reduction, in which case the output ``y`` will have the
          same shape as the inputs ``x`` and ``t``.
  
          >>> import numpy as np
          >>> from chainer import functions as F
          >>> x = np.array([[-2.0, 3.0, 0.5], [5.0, 2.0, -0.5]]).\
  astype(np.float32)
          >>> x.shape
          (2, 3)
          >>> t = np.array([[-2.0, 3.0, 0.0], [10.0, 2.0, -0.5]]).\
  astype(np.float32)
          >>> t.shape
          (2, 3)
          >>> y = F.huber_loss(x, t, delta=1.0, reduce='no')
          >>> y.shape
          (2, 3)
          >>> y
          variable([[0.   , 0.   , 0.125],
                    [4.5  , 0.   , 0.   ]])
  
          Example with reduction along the second axis.
  
          >>> y = F.huber_loss(x, t, delta=1.0, reduce='sum_along_second_axis')
          >>> y.shape
          (2,)
          >>> y
          variable([0.125, 4.5  ])
  
      """
      return HuberLoss(delta=delta, reduce=reduce).apply((x, t))[0]
  
file_path: chainer/chainer/functions/loss/huber_loss.py
imports: import chainer
  from chainer import backend
  from chainer import function_node
  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: huber_loss(x, t, delta, reduce) and returns: HuberLoss