class_docstr: ResNetV1b BasicBlockV1b
      
code: def __init__(
      self,
      planes,
      strides=1,
      dilation=1,
      downsample=None,
      previous_dilation=1,
      norm_layer=None,
      norm_kwargs=None,
      **kwargs,
  ):
      super(BasicBlockV1b, self).__init__()
      norm_kwargs = norm_kwargs if norm_kwargs is not None else {}
      self.conv1 = nn.Conv2D(
          channels=planes,
          kernel_size=3,
          strides=strides,
          padding=dilation,
          dilation=dilation,
          use_bias=False,
      )
      self.bn1 = norm_layer(
          in_channels=planes,
          **({} if norm_kwargs is None else norm_kwargs),
      )
      self.relu1 = nn.Activation("relu")
      self.conv2 = nn.Conv2D(
          channels=planes,
          kernel_size=3,
          strides=1,
          padding=previous_dilation,
          dilation=previous_dilation,
          use_bias=False,
      )
      self.bn2 = norm_layer(
          in_channels=planes,
          **({} if norm_kwargs is None else norm_kwargs),
      )
      self.relu2 = nn.Activation("relu")
      self.downsample = downsample
      self.strides = strides
  
imports: from __future__ import division
  from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
label_desc: Use image node_id 1 to create a new BasicBlockV1b object from inherited base classes: HybridBlock with example: obj = BasicBlockV1b(planes, strides, dilation, downsample, previous_dilation, norm_layer, norm_kwargs)