class_docstr: Base class for all models
class_docstr_tok: ["Base","class","for","all","models"]
docstr: Calculate the training loss for a batch data.
  
  Args:
      interaction (Interaction): Interaction class of the batch.
  
  Returns:
      torch.Tensor: Training loss, shape: []
docstr_tok: ["Calculate","the","training","loss","for","a","batch","data",".","Args",":","interaction","(","Interaction",")",":","Interaction","class","of","the","batch",".","Returns",":","torch.Tensor",":","Training","loss",",","shape",":","[","]"]
code: def calculate_loss(self, interaction):
      r"""Calculate the training loss for a batch data.
  
      Args:
          interaction (Interaction): Interaction class of the batch.
  
      Returns:
          torch.Tensor: Training loss, shape: []
      """
      raise NotImplementedError
  
imports: from logging import getLogger
  import numpy
  import torch
  import torch.nn
  from recbole.model.layers import FMEmbedding, FMFirstOrderLinear, FLEmbedding
  from recbole.utils import ModelType, InputType, FeatureSource, FeatureType, set_color
label_desc: Use image node_id 2 for calling the AbstractRecommender obj's underlying member method code with example usage: obj.calculate_loss(interaction) without return types