class_docstr: A convolution, batch norm, ReLU block.
  
  A block in a feedforward network that performs a
  convolution followed by batch normalization followed
  by a ReLU activation.
  
  For the convolution operation, a square filter size is used.
  
  Args:
      out_channels (int): The number of output channels.
      ksize (int): The size of the filter is ksize x ksize.
      pad (int): The padding to use for the convolution.
class_docstr_tok: ["A","convolution",",","batch","norm",",","ReLU","block",".","A","block","in","a","feedforward","network","that","performs","a","convolution","followed","by","batch","normalization","followed","by","a","ReLU","activation",".","For","the","convolution","operation",",","a","square","filter","size","is","used",".","Args",":","out_channels","(","int",")",":","The","number","of","output","channels",".","ksize","(","int",")",":","The","size","of","the","filter","is","ksize","x","ksize",".","pad","(","int",")",":","The","padding","to","use","for","the","convolution","."]
code: def forward(self, x):
      h = self.conv(x)
      h = self.bn(h)
      return F.relu(h)
  
file_path: chainer/examples/cifar/models/VGG.py
imports: import chainer
  import chainer.functions
  import chainer.links
label_desc: Use image node_id 2 for calling the Block obj's underlying member method code with example usage: obj.forward(x) and returns: F