class_docstr: This is the basic RegNet construction class. It constructs the RegNet
  model stem, block creation. The RegNetParams / AnyNetParams are used
  to configure the model.
code: def create_stem(self, params: Union[RegNetParams, AnyNetParams]):
      # get the activation
      silu = None if get_torch_version() < [1, 7] else nn.SiLU()
      activation = {
          ActivationType.RELU: nn.ReLU(params.relu_in_place),
          ActivationType.SILU: silu,
      }[params.activation]
  
      # create stem
      stem = {
          StemType.RES_STEM_CIFAR: ResStemCifar,
          StemType.RES_STEM_IN: ResStemIN,
          StemType.SIMPLE_STEM_IN: SimpleStemIN,
      }[params.stem_type](
          3,
          params.stem_width,
          params.bn_epsilon,
          params.bn_momentum,
          activation,
      )
      init_weights(stem)
      return stem
  
imports: import math
  from collections import OrderedDict
  from typing import List, Tuple, Union
  import torch
  import torch.nn
  from classy_vision.generic.util import get_torch_version
  from classy_vision.models.anynet import ActivationType, AnyNetParams, BlockType, ResBasicBlock, ResBottleneckBlock, ResBottleneckLinearBlock, ResStemCifar, ResStemIN, SimpleStemIN, StemType, VanillaBlock
  from classy_vision.models.regnet import RegNetParams
  from fairscale.nn import checkpoint_wrapper
  from vissl.config import AttrDict
  from vissl.data.collators.collator_helper import MultiDimensionalTensor
  from vissl.models.model_helpers import Flatten, get_trunk_forward_outputs, get_tunk_forward_interpolated_outputs, transform_model_input_data_type
  from vissl.models.trunks import register_model_trunk
  from vissl.utils.fsdp_utils import auto_wrap_big_layers, fsdp_auto_wrap_bn, fsdp_wrapper
  from vissl.utils.misc import set_torch_seed
label_desc: Use image node_id 1 for calling the RegnetBlocksFactory obj's underlying member method code with example usage: obj.create_stem(params) and returns: stem