code: def __init__(
      self, in_channels: int, out_channels: int, **kwargs: Any
  ) -> None:
      super().__init__()
      self.conv = nn.Conv2d(
          in_channels, out_channels, bias=False, **kwargs
      )
      self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
  
imports: import warnings
  from collections import namedtuple
  from functools import partial
  from typing import Any, Callable, List, Optional, Tuple
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
  from ..transforms._presets import ImageClassification
  from ..utils import _log_api_usage_once
  from ._api import register_model, Weights, WeightsEnum
  from ._meta import _IMAGENET_CATEGORIES
  from ._utils import _ovewrite_named_param, handle_legacy_interface
label_desc: Use image node_id 1 to create a new BasicConv2d object from inherited base classes: nn with example: obj = BasicConv2d(in_channels, out_channels)