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 _initialize_params(self, in_channels, in_size):
      kh, kw = _pair(self.ksize)
      ih, iw = _pair(in_size)
      oh = _conv_output_length(ih, kh, self.stride[0])
      ow = _conv_output_length(iw, kw, self.stride[1])
      W_shape = (self.out_channels, oh, ow, in_channels, kh, kw)
      bias_shape = (
          self.out_channels,
          oh,
          ow,
      )
      self.W.initialize(W_shape)
      if not self.nobias:
          self.b.initialize(bias_shape)
  
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 2 for calling the LocalConvolution2D obj's underlying member method code with example usage: obj._initialize_params(in_channels, in_size) without return types