class_docstr: ALSTM Model
  
  Parameters
  ----------
  d_feat : int
      input dimension for each time step
  metric: str
      the evaluation metric used in early stop
  optimizer : str
      optimizer name
  GPU : int
      the GPU ID used for training
class_docstr_tok: ["ALSTM","Model","Parameters","--","--","--","--","--","d_feat",":","int","input","dimension","for","each","time","step","metric",":","str","the","evaluation","metric","used","in","early","stop","optimizer",":","str","optimizer","name","GPU",":","int","the","GPU","ID","used","for","training"]
code: def mse(self, pred, label, weight):
      loss = weight * (pred - label) ** 2
      return torch.mean(loss)
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import pandas
  from typing import Text, Union
  import copy
  from ...utils import get_or_create_path
  from ...log import get_module_logger
  import torch
  import torch.nn
  import torch.optim
  from torch.utils.data import DataLoader
  from .pytorch_utils import count_parameters
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
  from ...model.utils import ConcatDataset
  from ...data.dataset.weight import Reweighter
label_desc: Use image node_id 3 for calling the ALSTM obj's underlying member method code with example usage: obj.mse(pred, label, weight) and returns: torch