code: def __init__(
      self,
      depths: Sequence[int],
      widths: Sequence[int],
      group_widths: Sequence[int],
      bottleneck_multipliers: Sequence[int],
      strides: Sequence[int],
      stem_type: StemType = StemType.SIMPLE_STEM_IN,
      stem_width: int = 32,
      block_type: BlockType = BlockType.RES_BOTTLENECK_BLOCK,
      activation: ActivationType = ActivationType.RELU,
      use_se: bool = True,
      se_ratio: float = 0.25,
      bn_epsilon: float = 1e-05,
      bn_momentum: bool = 0.1,
  ):
      self.depths = depths
      self.widths = widths
      self.group_widths = group_widths
      self.bottleneck_multipliers = bottleneck_multipliers
      self.strides = strides
      self.stem_type = stem_type
      self.stem_width = stem_width
      self.block_type = block_type
      self.activation = activation
      self.use_se = use_se
      self.se_ratio = se_ratio if use_se else None
      self.bn_epsilon = bn_epsilon
      self.bn_momentum = bn_momentum
      self.relu_in_place = RELU_IN_PLACE
  
imports: import math
  from collections import OrderedDict
  from enum import auto, Enum
  from typing import Any, Dict, Optional, Sequence
  import torch.nn
  from classy_vision.generic.util import get_torch_version
  from classy_vision.models import ClassyModel, register_model
  from classy_vision.models.squeeze_and_excitation_layer import SqueezeAndExcitationLayer
label_desc: Use image node_id 1 to create a new AnyNetParams object  with example: obj = AnyNetParams(depths, widths, group_widths, bottleneck_multipliers, strides, stem_type, stem_width, block_type, activation, use_se, se_ratio, bn_epsilon, bn_momentum)