class_docstr: ResNet Basic Block for R2Plus1D
  
  Parameters
  ----------
  inplanes : int.
      Input channels of each block.
  planes : int.
      Output channels of each block.
  stride : int, default is 1.
      Stride in each dimension of 3D convolutional layers in a block.
  downsample : bool.
      Whether to contain a downsampling layer in the block.
  norm_layer : object
      Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
      Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
  norm_kwargs : dict
      Additional `norm_layer` arguments, for example `num_devices=4`
      for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
  layer_name : str, default is ''.
      Give a name to current block.
class_docstr_tok: ["ResNet","Basic","Block","for","R2Plus1D","Parameters","--","--","--","--","--","inplanes",":","int",".","Input","channels","of","each","block",".","planes",":","int",".","Output","channels","of","each","block",".","stride",":","int",",","default","is","1",".","Stride","in","each","dimension","of","3D","convolutional","layers","in","a","block",".","downsample",":","bool",".","Whether","to","contain","a","downsampling","layer","in","the","block",".","norm_layer",":","object","Normalization","layer","used","(","default",":",":","class",":","`","mxnet.gluon.nn.BatchNorm","`",")","Can","be",":","class",":","`","mxnet.gluon.nn.BatchNorm","`","or",":","class",":","`","mxnet.gluon.contrib.nn.SyncBatchNorm","`",".","norm_kwargs",":","dict","Additional","`","norm_layer","`","arguments",",","for","example","`","num_devices=4","`","for",":","class",":","`","mxnet.gluon.contrib.nn.SyncBatchNorm","`",".","layer_name",":","str",",","default","is","``",".","Give","a","name","to","current","block","."]
code: def __init__(
      self,
      inplanes,
      planes,
      stride=1,
      downsample=None,
      norm_layer=BatchNorm,
      norm_kwargs=None,
      layer_name="",
      **kwargs,
  ):
      super(BasicBlock, self).__init__()
      self.downsample = downsample
  
      with self.name_scope():
          midplanes = (inplanes * planes * 3 * 3 * 3) // (
              inplanes * 3 * 3 + 3 * planes
          )
          self.conv1 = Conv2Plus1D(inplanes, planes, midplanes, stride)
          self.bn1 = norm_layer(
              in_channels=planes,
              **({} if norm_kwargs is None else norm_kwargs),
          )
          self.relu = nn.Activation("relu")
          self.conv2 = Conv2Plus1D(planes, planes, midplanes)
          self.bn2 = norm_layer(
              in_channels=planes,
              **({} if norm_kwargs is None else norm_kwargs),
          )
  
imports: from mxnet import init
  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 BasicBlock object from inherited base classes: HybridBlock with example: obj = BasicBlock(inplanes, planes, stride, downsample, norm_layer, norm_kwargs, layer_name)