class_docstr: FastSEResNet 
class_docstr_tok: ["FastSEResNet"]
code: def __init__(self, architecture, norm_layer=nn.BatchNorm, **kwargs):
      super(FastSEResNet, self).__init__()
      architecture = architecture.split("_")[0]
      assert architecture in ["resnet50", "resnet101"]
      self.inplanes = 64
      self.norm_layer = norm_layer
      self.layers = [
          3,
          4,
          {"resnet50": 6, "resnet101": 23}[architecture],
          3,
      ]
      self.block = Bottleneck
  
      self.conv1 = nn.Conv2D(
          64,
          in_channels=3,
          kernel_size=7,
          strides=2,
          padding=3,
          use_bias=False,
          weight_initializer=initializer.Uniform(
              scale=math.sqrt(1 / (3 * 7 * 7))
          ),
          bias_initializer=initializer.Uniform(
              scale=math.sqrt(1 / (3 * 7 * 7))
          ),
      )
      self.bn1 = self.norm_layer(
          gamma_initializer=ZeroUniform(), **kwargs
      )
      self.relu = nn.Activation("relu")
      self.maxpool = nn.MaxPool2D(pool_size=3, strides=2, padding=1)
  
      self.layer1 = self.make_layer(
          self.block, 64, self.layers[0], **kwargs
      )
      self.layer2 = self.make_layer(
          self.block, 128, self.layers[1], stride=2, **kwargs
      )
      self.layer3 = self.make_layer(
          self.block, 256, self.layers[2], stride=2, **kwargs
      )
  
      self.layer4 = self.make_layer(
          self.block, 512, self.layers[3], stride=2, **kwargs
      )
  
imports: import math
  import os
  import mxnet
  from mxnet import initializer
  from mxnet.gluon import nn
  from mxnet.gluon.block import HybridBlock
  from .utils import ZeroUniform, _try_load_parameters
label_desc: Use image node_id 1 to create a new FastSEResNet object from inherited base classes: HybridBlock with example: obj = FastSEResNet(architecture, norm_layer)