class_docstr: ResNet stem for ImageNet: 7x7, BN, ReLU, MaxPool.
class_docstr_tok: ["ResNet","stem","for","ImageNet",":","7x7",",","BN",",","ReLU",",","MaxPool","."]
code: def __init__(
      self,
      width_in: int,
      width_out: int,
      bn_epsilon: float,
      bn_momentum: float,
      activation: nn.Module,
  ):
      super().__init__()
      self.stem = nn.Sequential(
          nn.Conv2d(
              width_in, width_out, 7, stride=2, padding=3, bias=False
          ),
          nn.BatchNorm2d(
              width_out, eps=bn_epsilon, momentum=bn_momentum
          ),
          activation,
          nn.MaxPool2d(3, stride=2, padding=1),
      )
      self.depth = 3
  
imports: import math
  from collections import OrderedDict
  from enum import auto, Enum
  from typing import Any, Dict, Optional, Sequence
  import torch.nn
  from classy_vision.generic.util import get_torch_version
  from classy_vision.models import ClassyModel, register_model
  from classy_vision.models.squeeze_and_excitation_layer import SqueezeAndExcitationLayer
label_desc: Use image node_id 1 to create a new ResStemIN object from inherited base classes: nn with example: obj = ResStemIN(width_in, width_out, bn_epsilon, bn_momentum, activation)