class_docstr: Linear regression fun
class_docstr_tok: ["Linear","regression","fun"]
code: def __init__(self, d_inputs, d_outputs):
      # Perform initialization of the pytorch superclass
      super(FlatNet, self).__init__()
      self.layers = nn.Sequential(
          nn.Linear(d_inputs, d_outputs),
      )
      nn.init.kaiming_normal_(self.layers[0].weight, mode="fan_in")
  
imports: import logging
  import math
  from collections import OrderedDict
  from functools import reduce
  from typing import Dict, List, Optional, Union
  import numpy
  import pytorch_lightning
  import torch
  import torch.nn
  import torchmetrics
  from neuralprophet import configure, np_types
  from neuralprophet.components.router import get_future_regressors, get_seasonality, get_trend
  from neuralprophet.utils import check_for_regularization, config_events_to_model_dims, reg_func_events, reg_func_regressors, reg_func_season, reg_func_trend
  from neuralprophet.utils_torch import init_parameter, interprete_model
label_desc: Use image node_id 1 to create a new FlatNet object from inherited base classes: nn with example: obj = FlatNet(d_inputs, d_outputs)