class_docstr: The collector is used to collect the resource for evaluator.
  As the evaluation metrics are various, the needed resource not only contain the recommended result
  but also other resource from data and model. They all can be collected by the collector during the training
  and evaluation process.
  
  This class is only used in Trainer.
docstr: Collect the evaluation resource from total output and label.
  It was designed for those models that can not predict with batch.
  Args:
      eval_pred (torch.Tensor): the output score tensor of model.
      data_label (torch.Tensor): the label tensor.
docstr_tok: ["Collect","the","evaluation","resource","from","total","output","and","label",".","It","was","designed","for","those","models","that","can","not","predict","with","batch",".","Args",":","eval_pred","(","torch.Tensor",")",":","the","output","score","tensor","of","model",".","data_label","(","torch.Tensor",")",":","the","label","tensor","."]
code: def eval_collect(
      self, eval_pred: torch.Tensor, data_label: torch.Tensor
  ):
      """Collect the evaluation resource from total output and label.
      It was designed for those models that can not predict with batch.
      Args:
          eval_pred (torch.Tensor): the output score tensor of model.
          data_label (torch.Tensor): the label tensor.
      """
      if self.register.need("rec.score"):
          self.data_struct.update_tensor("rec.score", eval_pred)
  
      if self.register.need("data.label"):
          self.label_field = self.config["LABEL_FIELD"]
          self.data_struct.update_tensor(
              "data.label", data_label.to(self.device)
          )
  
imports: from recbole.evaluator.register import Register
  import torch
  import copy
label_desc: Use image node_id 6 for calling the Collector obj's underlying member method code with example usage: obj.eval_collect(eval_pred, data_label) without return types