class_docstr: The :class:`GroupConv2d` class is 2D grouped convolution, see `here <https://blog.yani.io/filter-group-tutorial/>`__.
  
  Parameters
  --------------
  n_filter : int
      The number of filters.
  filter_size : tuple of int
      The filter size.
  strides : tuple of int
      The stride step.
  n_group : int
      The number of groups.
  act : activation function
      The activation function of this layer.
  padding : str
      The padding algorithm type: "SAME" or "VALID".
  data_format : str
      "channels_last" (NHWC, default) or "channels_first" (NCHW).
  dilation_rate : tuple of int
      Specifying the dilation rate to use for dilated convolution.
  W_init : initializer
      The initializer for the weight matrix.
  b_init : initializer or None
      The initializer for the bias vector. If None, skip biases.
  in_channels : int
      The number of in channels.
  name : None or str
      A unique layer name.
  
  Examples
  ---------
  With TensorLayer
  
  >>> net = tl.layers.Input([8, 24, 24, 32], name='input')
  >>> groupconv2d = tl.layers.QuanConv2d(
  ...     n_filter=64, filter_size=(3, 3), strides=(2, 2), n_group=2, name='group'
  ... )(net)
  >>> print(groupconv2d)
  >>> output shape : (8, 12, 12, 64)
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code: def __init__(
      self,
      n_filter=32,
      filter_size=(3, 3),
      strides=(2, 2),
      n_group=2,
      act=None,
      padding="SAME",
      data_format="channels_last",
      dilation_rate=(1, 1),
      W_init=tl.initializers.truncated_normal(stddev=0.02),
      b_init=tl.initializers.constant(value=0.0),
      in_channels=None,
      name=None,  # 'groupconv',
  ):  # Windaway
      super().__init__(name, act=act)
      self.n_filter = n_filter
      self.filter_size = filter_size
      self.strides = self._strides = strides
      self.n_group = n_group
      self.padding = padding
      self.data_format = data_format
      self.dilation_rate = self._dilation_rate = dilation_rate
      self.W_init = W_init
      self.b_init = b_init
      self.in_channels = in_channels
  
      if self.in_channels:
          self.build(None)
          self._built = True
  
      logging.info(
          "GroupConv2d %s: n_filter: %d size: %s strides: %s n_group: %d pad: %s act: %s"
          % (
              self.name,
              n_filter,
              str(filter_size),
              str(strides),
              n_group,
              padding,
              self.act.__name__
              if self.act is not None
              else "No Activation",
          )
      )
  
file_path: TensorLayer/tensorlayer/layers/convolution/group_conv.py
imports: import tensorflow
  import tensorlayer
  from tensorlayer import logging
  from tensorlayer.decorators import deprecated_alias
  from tensorlayer.layers.core import Layer
label_desc: Use image node_id 1 to create a new GroupConv2d object from inherited base classes: Layer with example: obj = GroupConv2d(n_filter, filter_size, strides, n_group, act, padding, data_format, dilation_rate, W_init, b_init, in_channels, name)