class_docstr: Similar to RegNet trunk, but with FSDP enabled.
  
  Later, FSDP + vision model is ready, this can be migrated into classy_vision.
code: def __init__(self, model_config: AttrDict, model_name: str):
      super().__init__()
      self.model_config = model_config
      self.seed = self.model_config._MODEL_INIT_SEED
      self.use_activation_checkpointing = (
          model_config.ACTIVATION_CHECKPOINTING.USE_ACTIVATION_CHECKPOINTING
      )
      with set_torch_seed(self.seed):
          (
              self._feature_blocks,
              self.trunk_depth,
          ) = create_regnet_feature_blocks(
              factory=RegnetFSDPBlocksFactory(
                  fsdp_config=self.model_config.FSDP_CONFIG
              ),
              model_config=self.model_config,
          )
  
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 to create a new _RegNetFSDP object from inherited base classes: nn with example: obj = _RegNetFSDP(model_config, model_name)