code: def trend_model(thetas, t):
      p = thetas.size()[-1]
      from bigdl.nano.utils.common import invalidInputError
  
      invalidInputError(p <= 4, "thetas_dim is too big.")
      T = torch.tensor([t**i for i in range(p)]).float()
      return thetas.mm(T)
  
imports: import pickle
  import random
  from time import time
  from typing import Union
  import numpy
  import torch
  from torch import nn, optim
  from torch.nn import functional
  from torch.nn.functional import mse_loss, l1_loss, binary_cross_entropy, cross_entropy
  from torch.optim import Optimizer
  from .utils import PYTORCH_REGRESSION_LOSS_MAP
  from pytorch_lightning import seed_everything
label_desc: Use image node_id 2 for calling a global function with example usage: trend_model(thetas, t) and returns: thetas