code: def __init__(
      self,
      in_channels,
      ch_head=256,
      ch_emb=128,
      num_classes=1,
      num_identities_dict={0: 14455},
  ):
      super(FairMOTEmbeddingHead, self).__init__()
      assert num_classes >= 1
      self.num_classes = num_classes
      self.ch_emb = ch_emb
      self.num_identities_dict = num_identities_dict
      self.reid = nn.Sequential(
          ConvLayer(
              in_channels, ch_head, kernel_size=3, padding=1, bias=True
          ),
          nn.ReLU(),
          ConvLayer(
              ch_head,
              ch_emb,
              kernel_size=1,
              stride=1,
              padding=0,
              bias=True,
          ),
      )
      param_attr = paddle.ParamAttr(initializer=KaimingUniform())
      bound = 1 / math.sqrt(ch_emb)
      bias_attr = paddle.ParamAttr(initializer=Uniform(-bound, bound))
      self.reid_loss = nn.CrossEntropyLoss(
          ignore_index=-1, reduction="sum"
      )
  
      if num_classes == 1:
          nID = self.num_identities_dict[0]  # single class
          self.classifier = nn.Linear(
              ch_emb, nID, weight_attr=param_attr, bias_attr=bias_attr
          )
          # When num_identities(nID) is 1, emb_scale is set as 1
          self.emb_scale = (
              math.sqrt(2) * math.log(nID - 1) if nID > 1 else 1
          )
      else:
          self.classifiers = dict()
          self.emb_scale_dict = dict()
          for cls_id, nID in self.num_identities_dict.items():
              self.classifiers[str(cls_id)] = nn.Linear(
                  ch_emb,
                  nID,
                  weight_attr=param_attr,
                  bias_attr=bias_attr,
              )
              # When num_identities(nID) is 1, emb_scale is set as 1
              self.emb_scale_dict[str(cls_id)] = (
                  math.sqrt(2) * math.log(nID - 1) if nID > 1 else 1
              )
  
file_path: PaddleDetection/ppdet/modeling/reid/fairmot_embedding_head.py
imports: import numpy
  import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import KaimingUniform, Uniform
  from ppdet.core.workspace import register
  from ppdet.modeling.heads.centernet_head import ConvLayer
label_desc: Use image node_id 1 to create a new FairMOTEmbeddingHead object from inherited base classes: nn with example: obj = FairMOTEmbeddingHead(in_channels, ch_head, ch_emb, num_classes, num_identities_dict)