class_docstr: RegNet backbone.
  
  More details can be found in `paper <https://arxiv.org/abs/2003.13678>`_ .
  
  Args:
      arch (dict): The parameter of RegNets.
  
          - w0 (int): initial width
          - wa (float): slope of width
          - wm (float): quantization parameter to quantize the width
          - depth (int): depth of the backbone
          - group_w (int): width of group
          - bot_mul (float): bottleneck ratio, i.e. expansion of bottleneck.
      strides (Sequence[int]): Strides of the first block of each stage.
      base_channels (int): Base channels after stem layer.
      in_channels (int): Number of input image channels. Default: 3.
      dilations (Sequence[int]): Dilation of each stage.
      out_indices (Sequence[int]): Output from which stages.
      style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
          layer is the 3x3 conv layer, otherwise the stride-two layer is
          the first 1x1 conv layer.
      frozen_stages (int): Stages to be frozen (all param fixed). -1 means
          not freezing any parameters.
      norm_cfg (dict): dictionary to construct and config norm layer.
      norm_eval (bool): Whether to set norm layers to eval mode, namely,
          freeze running stats (mean and var). Note: Effect on Batch Norm
          and its variants only.
      with_cp (bool): Use checkpoint or not. Using checkpoint will save some
          memory while slowing down the training speed.
      zero_init_residual (bool): whether to use zero init for last norm layer
          in resblocks to let them behave as identity.
      pretrained (str, optional): model pretrained path. Default: None
      init_cfg (dict or list[dict], optional): Initialization config dict.
          Default: None
  
  Example:
      >>> from mmdet.models import RegNet
      >>> import torch
      >>> self = RegNet(
              arch=dict(
                  w0=88,
                  wa=26.31,
                  wm=2.25,
                  group_w=48,
                  depth=25,
                  bot_mul=1.0))
      >>> self.eval()
      >>> inputs = torch.rand(1, 3, 32, 32)
      >>> level_outputs = self.forward(inputs)
      >>> for level_out in level_outputs:
      ...     print(tuple(level_out.shape))
      (1, 96, 8, 8)
      (1, 192, 4, 4)
      (1, 432, 2, 2)
      (1, 1008, 1, 1)
code: def _make_stem_layer(self, in_channels, base_channels):
      self.conv1 = build_conv_layer(
          self.conv_cfg,
          in_channels,
          base_channels,
          kernel_size=3,
          stride=2,
          padding=1,
          bias=False,
      )
      self.norm1_name, norm1 = build_norm_layer(
          self.norm_cfg, base_channels, postfix=1
      )
      self.add_module(self.norm1_name, norm1)
      self.relu = nn.ReLU(inplace=True)
  
file_path: mmdetection/mmdet/models/backbones/regnet.py
imports: import warnings
  import numpy
  import torch.nn
  from mmcv.cnn import build_conv_layer, build_norm_layer
  from mmdet.registry import MODELS
  from .resnet import ResNet
  from .resnext import Bottleneck
label_desc: Use image node_id 2 for calling the RegNet obj's underlying member method code with example usage: obj._make_stem_layer(in_channels, base_channels) without return types