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 forward(
      self, x, out_feat_keys: List[str] = None
  ) -> List[torch.Tensor]:
      if isinstance(x, MultiDimensionalTensor):
          out = get_tunk_forward_interpolated_outputs(
              input_type=self.model_config.INPUT_TYPE,
              interpolate_out_feat_key_name="res5",
              remove_padding_before_feat_key_name="avgpool",
              feat=x,
              feature_blocks=self._feature_blocks,
              # FSDP has its own activation checkpoint method: disable vissl's method here.
              use_checkpointing=False,
              checkpointing_splits=0,
          )
      else:
          model_input = transform_model_input_data_type(
              x, self.model_config.INPUT_TYPE
          )
          out = get_trunk_forward_outputs(
              feat=model_input,
              out_feat_keys=out_feat_keys,
              feature_blocks=self._feature_blocks,
              # FSDP has its own activation checkpoint method: disable vissl's method here.
              use_checkpointing=False,
              checkpointing_splits=0,
          )
      return out
  
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 2 for calling the _RegNetFSDP obj's underlying member method code with example usage: obj.forward(x, out_feat_keys) and returns: out