class_docstr: Basic transformation: Tx3x3, 1x3x3, where T is the size of temporal kernel.
class_docstr_tok: ["Basic","transformation",":","Tx3x3",",","1x3x3",",","where","T","is","the","size","of","temporal","kernel","."]
code: def _construct(self, dim_in, dim_out, stride, dilation, norm_module):
      # Tx3x3, BN, ReLU.
      self.a = nn.Conv3d(
          dim_in,
          dim_out,
          kernel_size=[self.temp_kernel_size, 3, 3],
          stride=[1, stride, stride],
          padding=[int(self.temp_kernel_size // 2), 1, 1],
          bias=False,
      )
      self.a_bn = norm_module(
          num_features=dim_out, eps=self._eps, momentum=self._bn_mmt
      )
      self.a_relu = nn.ReLU(inplace=self._inplace_relu)
      # 1x3x3, BN.
      self.b = nn.Conv3d(
          dim_out,
          dim_out,
          kernel_size=[1, 3, 3],
          stride=[1, 1, 1],
          padding=[0, dilation, dilation],
          dilation=[1, dilation, dilation],
          bias=False,
      )
  
      self.b.final_conv = True
  
      self.b_bn = norm_module(
          num_features=dim_out, eps=self._eps, momentum=self._bn_mmt
      )
  
      self.b_bn.transform_final_bn = True
  
imports: import torch
  import torch.nn
  from slowfast.models.common import drop_path
  from slowfast.models.nonlocal_helper import Nonlocal
  from slowfast.models.operators import SE, Swish
label_desc: Use image node_id 2 for calling the BasicTransform obj's underlying member method code with example usage: obj._construct(dim_in, dim_out, stride, dilation, norm_module) without return types