class_docstr: Video R(2+1)D stem module. Provides stem operations of Conv, BN, ReLU, MaxPool
  on input data tensor for one or multiple pathways.
docstr: The `__init__` method of any subclass should also contain these
  arguments. List size of 1 for single pathway models (C2D, I3D, SlowOnly
  and etc), list size of 2 for two pathway models (SlowFast).
  
  Args:
      dim_in (list): the list of channel dimensions of the inputs.
      dim_out (list): the output dimension of the convolution in the stem
          layer.
      kernel (list): the kernels' size of the convolutions in the stem
          layers. Temporal kernel size, height kernel size, width kernel
          size in order.
      stride (list): the stride sizes of the convolutions in the stem
          layer. Temporal kernel stride, height kernel size, width kernel
          size in order.
      padding (list): the paddings' sizes of the convolutions in the stem
          layer. Temporal padding size, height padding size, width padding
          size in order.
      inplace_relu (bool): calculate the relu on the original input
          without allocating new memory.
      bn_eps (float): epsilon for batch norm.
      bn_mmt (float): momentum for batch norm. Noted that BN momentum in
          PyTorch = 1 - BN momentum in Caffe2.
      maxpool (iterable): At training time, when crop size is 224 x 224, do max
          pooling. When crop size is 112 x 112, skip max pooling.
          Default value is a (True,)
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code: def __init__(
      self,
      dim_in,
      dim_out,
      kernel,
      stride,
      padding,
      inplace_relu=True,
      bn_eps=1e-5,
      bn_mmt=0.1,
      maxpool=(True,),
  ):
      """
      The `__init__` method of any subclass should also contain these
      arguments. List size of 1 for single pathway models (C2D, I3D, SlowOnly
      and etc), list size of 2 for two pathway models (SlowFast).
  
      Args:
          dim_in (list): the list of channel dimensions of the inputs.
          dim_out (list): the output dimension of the convolution in the stem
              layer.
          kernel (list): the kernels' size of the convolutions in the stem
              layers. Temporal kernel size, height kernel size, width kernel
              size in order.
          stride (list): the stride sizes of the convolutions in the stem
              layer. Temporal kernel stride, height kernel size, width kernel
              size in order.
          padding (list): the paddings' sizes of the convolutions in the stem
              layer. Temporal padding size, height padding size, width padding
              size in order.
          inplace_relu (bool): calculate the relu on the original input
              without allocating new memory.
          bn_eps (float): epsilon for batch norm.
          bn_mmt (float): momentum for batch norm. Noted that BN momentum in
              PyTorch = 1 - BN momentum in Caffe2.
          maxpool (iterable): At training time, when crop size is 224 x 224, do max
              pooling. When crop size is 112 x 112, skip max pooling.
              Default value is a (True,)
      """
      super(R2Plus1DStemMultiPathway, self).__init__(
          dim_in,
          dim_out,
          kernel,
          stride,
          padding,
          inplace_relu=inplace_relu,
          bn_eps=bn_eps,
          bn_mmt=bn_mmt,
          maxpool=maxpool,
      )
  
imports: from typing import Sequence
  import torch.nn
  from .r2plus1_util import r2plus1_unit
label_desc: Use image node_id 1 to create a new R2Plus1DStemMultiPathway object from inherited base classes: ResNeXt3DStemMultiPathway with example: obj = R2Plus1DStemMultiPathway(dim_in, dim_out, kernel, stride, padding, inplace_relu, bn_eps, bn_mmt, maxpool)