class_docstr: Custom Conv Block.
  
  Parameters
  ----------
  in_channels : int
      Input channels of conv layer.
  out_channels : int
      Output channels of conv layer.
  use_dcnv2 : bool
      Whether use modulated deformable convolution(DCN version2).
  momentum : float, default is 0.9
      Momentum for Norm layer.
  norm_layer : nn.HybridBlock, default is BatchNorm.
      The norm_layer instance.
  norm_kwargs : dict
      Extra arguments for norm layer.
class_docstr_tok: ["Custom","Conv","Block",".","Parameters","--","--","--","--","--","in_channels",":","int","Input","channels","of","conv","layer",".","out_channels",":","int","Output","channels","of","conv","layer",".","use_dcnv2",":","bool","Whether","use","modulated","deformable","convolution","(","DCN","version2",")",".","momentum",":","float",",","default","is","0.9","Momentum","for","Norm","layer",".","norm_layer",":","nn.HybridBlock",",","default","is","BatchNorm",".","The","norm_layer","instance",".","norm_kwargs",":","dict","Extra","arguments","for","norm","layer","."]
code: def hybrid_forward(self, F, x):
      x = self.conv(x)
      x = self.actf(x)
      return x
  
imports: from __future__ import absolute_import
  import warnings
  import numpy
  from mxnet.context import cpu
  from mxnet.gluon import nn
  from mxnet.gluon import contrib
  from .deconv_resnet import BilinearUpSample
  from ..model_zoo import get_model
label_desc: Use image node_id 2 for calling the CustomConv obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: x