code: def get_alphapose(
      name,
      dataset,
      num_joints,
      pretrained=False,
      pretrained_base=True,
      ctx=mx.cpu(),
      norm_layer=nn.BatchNorm,
      norm_kwargs=None,
      root=os.path.join("~", ".mxnet", "models"),
      **kwargs,
  ):
      r"""Utility function to return AlphaPose networks.
  
      Parameters
      ----------
      name : str
          Model name.
      dataset : str
          The name of dataset.
      pretrained : bool or str
          Boolean value controls whether to load the default pretrained weights for model.
          String value represents the hashtag for a certain version of pretrained weights.
      ctx : mxnet.Context
          Context such as mx.cpu(), mx.gpu(0).
      root : str
          Model weights storing path.
  
      Returns
      -------
      mxnet.gluon.HybridBlock
          The AlphaPose network.
  
      """
      if norm_kwargs is None:
          norm_kwargs = {}
      preact = FastSEResNet(name, norm_layer=norm_layer, **norm_kwargs)
      if not pretrained and pretrained_base:
          from ..model_zoo import get_model
  
          base_network = get_model(name, pretrained=True, root=root)
          _try_load_parameters(self=base_network, model=base_network)
      net = AlphaPose(preact, num_joints, **kwargs)
      if pretrained:
          from ..model_store import get_model_file
  
          full_name = "_".join(("alpha_pose", name, dataset))
          net.load_parameters(
              get_model_file(full_name, tag=pretrained, root=root)
          )
      else:
          import warnings
  
          with warnings.catch_warnings(record=True):
              warnings.simplefilter("always")
              net.collect_params().initialize()
      net.collect_params().reset_ctx(ctx)
      return net
  
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 for calling a global function with example usage: get_alphapose(name, dataset, num_joints, pretrained, pretrained_base, ctx, norm_layer, norm_kwargs, root) and returns: net