code: def __init__(
      self,
      inplanes,
      planes,
      stride=1,
      downsample=None,
      groups=1,
      base_width=64,
      dilation=1,
      norm_layer=None,
      bit_width=8,
  ):
      super().__init__()
      if norm_layer is None:
          norm_layer = nn.BatchNorm2d
      width = int(planes * (base_width / 64.0)) * groups
      # Both self.conv2 and self.downsample layers downsample the input when stride != 1
      self.conv1 = conv1x1(inplanes, width, weight_bit_width=bit_width)
      self.bn1 = norm_layer(width)
      self.conv2 = conv3x3(
          width,
          width,
          stride,
          groups,
          dilation,
          weight_bit_width=bit_width,
      )
      self.bn2 = norm_layer(width)
      self.conv3 = conv1x1(
          width, planes * self.expansion, weight_bit_width=bit_width
      )
      self.bn3 = norm_layer(planes * self.expansion)
      self.relu = make_PACT_relu(bit_width=bit_width)
      self.downsample = downsample
      self.stride = stride
  
imports: import os
  from pathlib import Path
  import brevitas.nn
  import torch
  import torch.nn
  from pact import PACTReLU
  from torchvision import datasets, models
  from torchvision.transforms import Compose, Normalize, Pad, RandomCrop, RandomHorizontalFlip, ToTensor
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, groups, base_width, dilation, norm_layer, bit_width)