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 __init__(
      self,
      inplanes,
      planes,
      stride=1,
      downsample=None,
      dilation=1,
      head_conv=1,
  ):
      super().__init__()
      if head_conv == 1:
          self.conv1 = nn.Conv3d(
              inplanes, planes, kernel_size=1, bias=False
          )
          self.bn1 = BN(planes)
      elif head_conv == 3:
          self.conv1 = nn.Conv3d(
              inplanes,
              planes,
              kernel_size=(3, 1, 1),
              bias=False,
              padding=(1, 0, 0),
          )
          self.bn1 = BN(planes)
      else:
          raise ValueError("Unsupported head_conv!")
      self.conv2 = nn.Conv3d(
          planes,
          planes,
          kernel_size=(1, 3, 3),
          stride=(1, stride, stride),
          padding=(0, dilation, dilation),
          dilation=(1, dilation, dilation),
          bias=False,
      )
      self.bn2 = BN(planes)
      self.conv3 = nn.Conv3d(
          planes, planes * 4, kernel_size=1, bias=False
      )
      self.bn3 = BN(planes * 4)
      self.relu = nn.ReLU(inplace=True)
      self.downsample = downsample
      if downsample is not None:
          self.downsample_bn = BN(planes * 4)
      self.stride = stride
  
file_path: towhee/towhee/models/acar_net/backbone.py
imports: import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new Bottleneck object from inherited base classes: nn with example: obj = Bottleneck(inplanes, planes, stride, downsample, dilation, head_conv)