code: def __init__(self, in_channels, out_channels, bias=True):
      super(LinearConv, self).__init__()
  
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.linear = torch.nn.Linear(in_channels, out_channels, bias)
  
imports: import typing
  from .None import register_nas_space
  from .base import apply_fixed_architecture
  from .base import BaseSpace
  from ...model import BaseAutoModel
  from ....utils import get_logger
  from ..backend import *
  from ..utils import count_parameters, measure_latency
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import Module
  from .gasso_space import *
  from torch.autograd import Variable
  from collections import namedtuple
label_desc: Use image node_id 1 to create a new LinearConv object from inherited base classes: Module with example: obj = LinearConv(in_channels, out_channels, bias)