class_docstr: Slowfast Bottleneck
  
  Args:
      - inplanes (`int`):
          Number of input channels for conv layers.
      - planes (`int`):
          Number of output channels for conv layers.
      - stride (`int`):
          Number to build stride (1, stride, stride).
      - downsample (`nn.Module=None`):
          Down-sample method.
      - dilation (`int=1`):
          Number of dilation.
      - head_conv (`int=1`):
          Number to control head conv layer.
class_docstr_tok: ["Slowfast","Bottleneck","Args",":","-","inplanes","(","`","int","`",")",":","Number","of","input","channels","for","conv","layers",".","-","planes","(","`","int","`",")",":","Number","of","output","channels","for","conv","layers",".","-","stride","(","`","int","`",")",":","Number","to","build","stride","(","1",",","stride",",","stride",")",".","-","downsample","(","`","nn.Module=None","`",")",":","Down-sample","method",".","-","dilation","(","`","int=1","`",")",":","Number","of","dilation",".","-","head_conv","(","`","int=1","`",")",":","Number","to","control","head","conv","layer","."]
code: def forward(self, x):
      res = x
      out = self.conv1(x)
      out = self.bn1(out)
      out = self.relu(out)
  
      out = self.conv2(out)
      out = self.bn2(out)
      out = self.relu(out)
  
      out = self.conv3(out)
      out = self.bn3(out)
  
      if self.downsample is not None:
          res = self.downsample(x)
          res = self.downsample_bn(res)
  
      out = out + res
      out = self.relu(out)
  
      return out
  
file_path: towhee/towhee/models/acar_net/backbone.py
imports: import torch
  from torch import nn
label_desc: Use image node_id 2 for calling the Bottleneck obj's underlying member method code with example usage: obj.forward(x) and returns: out