class_docstr: Two-dimensional local convolutional layer.
  
  This link wraps the :func:`~chainer.functions.local_convolution_2d`
  function and holds the filter weight and bias array as parameters.
  
  Args:
      in_channels (int): Number of channels of input arrays. If either
          in_channels or in_size is ``None``,
          parameter initialization will be deferred until the first forward
          data pass at which time the size will be determined.
      out_channels (int): Number of channels of output arrays
      in_size (int or pair of ints): Size of each image channel
          ``in_size=k`` and ``in_size=(k,k)`` are equivalent. If either
          in_channels or in_size is ``None``, parameter initialization will
          be deferred until the first forward data pass when the size will be
          determined.
      ksize (int or pair of ints): Size of filters (a.k.a. kernels).
          ``ksize=k`` and ``ksize=(k, k)`` are equivalent.
      stride (int or pair of ints): Stride of filter applications.
          ``stride=s`` and ``stride=(s, s)`` are equivalent.
      nobias (bool): If ``True``, then this link does not use the bias term.
      initialW (:ref:`initializer <initializer>`): Initializer to
          initialize the weight. When it is :class:`numpy.ndarray`,
          its ``ndim`` should be 6.
      initial_bias (:ref:`initializer <initializer>`): Initializer to
          initialize the bias. If ``None``, the bias will be initialized to
          zero. When it is :class:`numpy.ndarray`, its ``ndim`` should be 3.
  
  .. seealso::
     See :func:`chainer.functions.local_convolution_2d`.
  
  Attributes:
      W (~chainer.Variable): Weight parameter.
      b (~chainer.Variable): Bias parameter.
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code: def __init__(
      self,
      in_channels,
      out_channels,
      in_size=None,
      ksize=None,
      stride=1,
      nobias=False,
      initialW=None,
      initial_bias=None,
      **kwargs,
  ):
      super(LocalConvolution2D, self).__init__()
      self.ksize = ksize
      self.stride = _pair(stride)
      self.nobias = nobias
      self.out_channels = out_channels
      with self.init_scope():
          W_initializer = initializers._get_initializer(initialW)
          self.W = variable.Parameter(W_initializer)
  
          if nobias:
              self.b = None
          else:
              if initial_bias is None:
                  initial_bias = 0
              bias_initializer = initializers._get_initializer(
                  initial_bias
              )
              self.b = variable.Parameter(bias_initializer)
  
          if in_channels is not None and in_size is not None:
              self._initialize_params(in_channels, _pair(in_size))
  
file_path: chainer/chainer/links/connection/local_convolution_2d.py
imports: from chainer.functions.connection import local_convolution_2d
  from chainer import initializers
  from chainer import link
  from chainer import variable
label_desc: Use image node_id 1 to create a new LocalConvolution2D object from inherited base classes: link with example: obj = LocalConvolution2D(in_channels, out_channels, in_size, ksize, stride, nobias, initialW, initial_bias)