class_docstr: Simply wrap the RegnetBlocksFactory with the FSDP
  This takes care of wrapping BN properly,
  initializing the weights etc.
code: def create_block(
      self,
      width_in: int,
      width_out: int,
      stride: int,
      params: Union[RegNetParams, AnyNetParams],
      bottleneck_multiplier: float,
      group_width: int = 1,
  ):
      block = super().create_block(
          width_in,
          width_out,
          stride,
          params,
          bottleneck_multiplier,
          group_width,
      )
      block = fsdp_auto_wrap_bn(block)
      if self.fsdp_config.AUTO_WRAP_THRESHOLD > 0:
          block = auto_wrap_big_layers(block, self.fsdp_config)
      block = fsdp_wrapper(module=block, **self.fsdp_config)
      return block
  
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 3 for calling the RegnetFSDPBlocksFactory obj's underlying member method code with example usage: obj.create_block(width_in, width_out, stride, params, bottleneck_multiplier, group_width) and returns: block