code: def __init__(
      self,
      base_name="resnet50_v1b",
      pretrained_base=False,
      pretrained_ctx=cpu(),
      num_joints=17,
      num_deconv_layers=3,
      num_deconv_filters=(256, 256, 256),
      num_deconv_kernels=(4, 4, 4),
      final_conv_kernel=1,
      deconv_with_bias=False,
      **kwargs,
  ):
      super(SimplePoseResNet, self).__init__(**kwargs)
  
      from ..model_zoo import get_model
  
      base_network = get_model(
          base_name,
          pretrained=pretrained_base,
          ctx=pretrained_ctx,
          norm_layer=gcv.nn.BatchNormCudnnOff,
      )
  
      self.resnet = nn.HybridSequential()
      if base_name.endswith("v1"):
          for layer in ["features"]:
              self.resnet.add(getattr(base_network, layer))
      else:
          for layer in [
              "conv1",
              "bn1",
              "relu",
              "maxpool",
              "layer1",
              "layer2",
              "layer3",
              "layer4",
          ]:
              self.resnet.add(getattr(base_network, layer))
  
      self.deconv_with_bias = deconv_with_bias
  
      # used for deconv layers
      self.deconv_layers = self._make_deconv_layer(
          num_deconv_layers,
          num_deconv_filters,
          num_deconv_kernels,
      )
  
      self.final_layer = nn.Conv2D(
          channels=num_joints,
          kernel_size=final_conv_kernel,
          strides=1,
          padding=1 if final_conv_kernel == 3 else 0,
          weight_initializer=initializer.Normal(0.001),
          bias_initializer=initializer.Zero(),
      )
  
file_path: gluon-cv/gluoncv/model_zoo/simple_pose/simple_pose_resnet.py
imports: from __future__ import division
  from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet import initializer
  import gluoncv
label_desc: Use image node_id 1 to create a new SimplePoseResNet object from inherited base classes: HybridBlock with example: obj = SimplePoseResNet(base_name, pretrained_base, pretrained_ctx, num_joints, num_deconv_layers, num_deconv_filters, num_deconv_kernels, final_conv_kernel, deconv_with_bias)