class_docstr: ResNe(X)t 3D stem module.
  Performs spatiotemporal Convolution, BN, and Relu following by a
      spatiotemporal pooling.
class_docstr_tok: ["ResNe","(","X",")","t","3D","stem","module",".","Performs","spatiotemporal","Convolution",",","BN",",","and","Relu","following","by","a","spatiotemporal","pooling","."]
code: def _construct_stem(self, dim_in, dim_out, norm_module):
      self.conv = nn.Conv3d(
          dim_in,
          dim_out,
          self.kernel,
          stride=self.stride,
          padding=self.padding,
          bias=False,
      )
      self.bn = norm_module(
          num_features=dim_out, eps=self.eps, momentum=self.bn_mmt
      )
      self.relu = nn.ReLU(self.inplace_relu)
      self.pool_layer = nn.MaxPool3d(
          kernel_size=[1, 3, 3], stride=[1, 2, 2], padding=[0, 1, 1]
      )
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the ResNetBasicStem obj's underlying member method code with example usage: obj._construct_stem(dim_in, dim_out, norm_module) without return types