class_docstr: The :class:`TernaryConv2d` class is a 2D ternary CNN layer, which weights are either -1 or 1 or 0 while inference.
  
  Note that, the bias vector would not be tenarized.
  
  Parameters
  ----------
  n_filter : int
      The number of filters.
  filter_size : tuple of int
      The filter size (height, width).
  strides : tuple of int
      The sliding window strides of corresponding input dimensions.
      It must be in the same order as the ``shape`` parameter.
  act : activation function
      The activation function of this layer.
  padding : str
      The padding algorithm type: "SAME" or "VALID".
  use_gemm : boolean
      If True, use gemm instead of ``tf.matmul`` for inference.
      TODO: support gemm
  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 the weight matrix.
  b_init : initializer or None
      The initializer for the 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, 12, 12, 32], name='input')
  >>> ternaryconv2d = tl.layers.QuanConv2d(
  ...     n_filter=64, filter_size=(5, 5), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='ternaryconv2d'
  ... )(net)
  >>> print(ternaryconv2d)
  >>> output shape : (8, 12, 12, 64)
code: def __repr__(self):
      actstr = (
          self.act.__name__ if self.act is not None else "No Activation"
      )
      s = (
          "{classname}(in_channels={in_channels}, out_channels={n_filter}, kernel_size={filter_size}"
          ", strides={strides}, padding={padding}"
      )
      if self.dilation_rate != (1,) * len(self.dilation_rate):
          s += ", dilation={dilation_rate}"
      if self.b_init is None:
          s += ", bias=False"
      s += ", " + actstr
      if self.name is not None:
          s += ", name='{name}'"
      s += ")"
      return s.format(
          classname=self.__class__.__name__, **self.__dict__
      )
  
file_path: TensorLayer/tensorlayer/layers/convolution/ternary_conv.py
imports: import tensorflow
  import tensorlayer
  from tensorlayer import logging
  from tensorlayer.decorators import deprecated_alias
  from tensorlayer.layers.core import Layer
  from tensorlayer.layers.utils import compute_alpha, ternary_operation
label_desc: Use image node_id 2 for calling the TernaryConv2d obj's underlying member method code with example usage: obj.__repr__() and returns: s