class_docstr: A ResNet class that is similar to torchvision's but contains the following changes:
  - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
  - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
  - The final pooling layer is a QKV attention instead of an average pool
  Args:
      layers (nn.Module): layers for modified ResNet
      output_dim (int): output dimension
      heads (int): output dimension
      input_resolution (int): input resolution
      width (int): output dimension
      vis (bool): visualization
code: def _make_layer(self, planes, blocks, stride=1):
      layers = [Bottleneck(self._inplanes, planes, stride)]
  
      self._inplanes = planes * Bottleneck.expansion
      for _ in range(1, blocks):
          layers.append(Bottleneck(self._inplanes, planes))
  
      return nn.Sequential(*layers)
  
imports: import os
  import warnings
  from collections import OrderedDict
  from typing import Tuple, Union, Callable
  import numpy
  from PIL import Image
  import torch
  from torch import nn
  from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
  from towhee.models.utils.download import download_from_url
  from towhee.models.clip.clip_utils import get_configs, convert_weights, patch_device, patch_float, tokenize
  from towhee.models.clip.auxilary import multi_head_attention_forward, MultiheadAttention
label_desc: Use image node_id 2 for calling the ModifiedResNet obj's underlying member method code with example usage: obj._make_layer(planes, blocks, stride) and returns: nn