code: def _docs_evaluation():
      _docs.set_doc(
          chainerx.accuracy,
          """accuracy(y, t, ignore_label=None)
  Computes multiclass classification accuracy of the minibatch.
  
  Args:
      y (~chainerx.ndarray):
          Array whose (i, j, k, ...)-th element indicates the score of
          the class j at the (i, k, ...)-th sample.
          The prediction label :math:`\\hat t` is calculated by the formula
          :math:`\\hat t(i, k, ...) = \\operatorname{\\mathrm{argmax}}_j \
  y(i, j, k, ...)`.
      t (~chainerx.ndarray):
          Array of ground truth labels.
      ignore_label (int or None): Skip calculating accuracy
          if the true label is ``ignore_label``.
  
  Returns:
      :func:`~chainerx.ndarray`: A variable holding a scalar \
  array of the accuracy.
  
  Note:
      This function is non-differentiable.
  
  .. seealso:: :func:`chainer.functions.accuracy`
  
  .. admonition:: Example
  
      We show the most common case, when ``y`` is the two dimensional array.
  
      >>> y = chainerx.array([[0.1, 0.7, 0.2], # prediction label is 1
      ...                     [8.0, 1.0, 2.0], # prediction label is 0
      ...                     [-8.0, 1.0, 2.0], # prediction label is 2
      ...                     [-8.0, -1.0, -2.0]]) # prediction label is 1
      >>> t = chainerx.array([1, 0, 2, 1], chainerx.int32)
      >>> chainerx.accuracy(y, t) \
  # 100% accuracy because all samples are correct
      array(1., shape=(), dtype=float64, device='native:0')
      >>> t = chainerx.array([1, 0, 0, 0], chainerx.int32)
      >>> chainerx.accuracy(y, t) \
  # 50% accuracy because 1st and 2nd samples are correct
      array(0.5, shape=(), dtype=float64, device='native:0')
      >>> chainerx.accuracy(y, t, ignore_label=0) \
  # 100% accuracy because of ignoring the 2nd, 3rd and 4th samples.
      array(1., shape=(), dtype=float64, device='native:0')
  """,
      )
  
file_path: chainer/chainerx/_docs/routines.py
imports: import chainerx
  from chainerx import _docs
label_desc: Use image node_id 3 for calling a global function with example usage: _docs_evaluation() without return types