code: def __init__(
      self,
      num_classes=1,
      num_identities=14455,  # dataset.num_identities_dict[0]
      anchor_levels=3,
      anchor_scales=4,
      embedding_dim=512,
      emb_loss="JDEEmbeddingLoss",
      jde_loss="JDELoss",
  ):
      super(JDEEmbeddingHead, self).__init__()
      self.num_classes = num_classes
      self.num_identities = num_identities
      self.anchor_levels = anchor_levels
      self.anchor_scales = anchor_scales
      self.embedding_dim = embedding_dim
      self.emb_loss = emb_loss
      self.jde_loss = jde_loss
  
      self.emb_scale = (
          math.sqrt(2) * math.log(self.num_identities - 1)
          if self.num_identities > 1
          else 1
      )
  
      self.identify_outputs = []
      self.loss_params_cls = []
      self.loss_params_reg = []
      self.loss_params_ide = []
      for i in range(self.anchor_levels):
          name = "identify_output.{}".format(i)
          identify_output = self.add_sublayer(
              name,
              nn.Conv2D(
                  in_channels=64
                  * (2**self.anchor_levels)
                  // (2**i),
                  out_channels=self.embedding_dim,
                  kernel_size=3,
                  stride=1,
                  padding=1,
                  bias_attr=ParamAttr(regularizer=L2Decay(0.0)),
              ),
          )
          self.identify_outputs.append(identify_output)
  
          loss_p_cls = self.add_sublayer(
              "cls.{}".format(i), LossParam(-4.15)
          )
          self.loss_params_cls.append(loss_p_cls)
          loss_p_reg = self.add_sublayer(
              "reg.{}".format(i), LossParam(-4.85)
          )
          self.loss_params_reg.append(loss_p_reg)
          loss_p_ide = self.add_sublayer(
              "ide.{}".format(i), LossParam(-2.3)
          )
          self.loss_params_ide.append(loss_p_ide)
  
      self.classifier = self.add_sublayer(
          "classifier",
          nn.Linear(
              self.embedding_dim,
              self.num_identities,
              weight_attr=ParamAttr(
                  learning_rate=1.0,
                  initializer=Normal(mean=0.0, std=0.01),
              ),
              bias_attr=ParamAttr(
                  learning_rate=2.0, regularizer=L2Decay(0.0)
              ),
          ),
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from ppdet.core.workspace import register
  from paddle.nn.initializer import Normal, Constant
label_desc: Use image node_id 1 to create a new JDEEmbeddingHead object from inherited base classes: nn with example: obj = JDEEmbeddingHead(num_classes, num_identities, anchor_levels, anchor_scales, embedding_dim, emb_loss, jde_loss)