class_docstr: Base class for all models
class_docstr_tok: ["Base","class","for","all","models"]
docstr: Model prints with number of trainable parameters
docstr_tok: ["Model","prints","with","number","of","trainable","parameters"]
code: def __str__(self):
      """
      Model prints with number of trainable parameters
      """
      model_parameters = filter(
          lambda p: p.requires_grad, self.parameters()
      )
      params = sum([np.prod(p.size()) for p in model_parameters])
      return (
          super().__str__()
          + set_color("\nTrainable parameters", "blue")
          + f": {params}"
      )
  
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 7 for calling the AbstractRecommender obj's underlying member method code with example usage: obj.__str__() and returns: unknown