class_docstr: VGG motibated cnn layers.
  
  Args:
      in_channel (int): Number of channels.
code: def __init__(self, in_channel=1):
      super(VGG2L, self).__init__()
      with self.init_scope():
          # CNN layer (VGG motivated)
          self.conv1_1 = L.Convolution2D(
              in_channel, 64, 3, stride=1, pad=1
          )
          self.conv1_2 = L.Convolution2D(64, 64, 3, stride=1, pad=1)
          self.conv2_1 = L.Convolution2D(64, 128, 3, stride=1, pad=1)
          self.conv2_2 = L.Convolution2D(128, 128, 3, stride=1, pad=1)
  
      self.in_channel = in_channel
  
imports: import logging
  import chainer
  import chainer.functions
  import chainer.links
  import numpy
  from chainer import cuda
  from espnet.nets.chainer_backend.nets_utils import _subsamplex
  from espnet.nets.e2e_asr_common import get_vgg2l_odim
label_desc: Use image node_id 1 to create a new VGG2L object from inherited base classes: chainer with example: obj = VGG2L(in_channel)