class_docstr: Res-net Basic Block
docstr: Args:
      inplanes (int): input channel
      planes (int): channels of the middle feature
      stride (int): stride of the convolution
      downsample (int): type of the down_sample
      norm_type (str): type of the normalization
      **kwargs (None): backup parameter
docstr_tok: ["Args",":","inplanes","(","int",")",":","input","channel","planes","(","int",")",":","channels","of","the","middle","feature","stride","(","int",")",":","stride","of","the","convolution","downsample","(","int",")",":","type","of","the","down_sample","norm_type","(","str",")",":","type","of","the","normalization","*","*","kwargs","(","None",")",":","backup","parameter"]
code: def __init__(
      self,
      inplanes,
      planes,
      stride=1,
      downsample=None,
      norm_type="BN",
      **kwargs,
  ):
      """
      Args:
          inplanes (int): input channel
          planes (int): channels of the middle feature
          stride (int): stride of the convolution
          downsample (int): type of the down_sample
          norm_type (str): type of the normalization
          **kwargs (None): backup parameter
      """
      super(BasicBlock, self).__init__()
      self.conv1 = self._conv3x3(inplanes, planes)
      self.bn1 = nn.BatchNorm(planes)
      self.conv2 = self._conv3x3(planes, planes)
      self.bn2 = nn.BatchNorm(planes)
      self.relu = nn.ReLU()
      self.downsample = downsample
      self.stride = stride
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  from paddle.nn.initializer import TruncatedNormal, Constant, Normal, KaimingNormal
label_desc: Use image node_id 1 to create a new BasicBlock object from inherited base classes: nn with example: obj = BasicBlock(inplanes, planes, stride, downsample, norm_type)