code: def __init__(
      self,
      model_name: str = "efficientnetv2_rw_s",
      pretrained=True,
      layers=None,
      activation: str = ACT_SILU,
      drop_rate=0.0,
      drop_path_rate=0.0,
      first_conv_stride_one: bool = False,
  ):
      from timm.models.factory import create_model
  
      act_layer = get_activation_block(activation)
      encoder = create_model(
          model_name=model_name,
          pretrained=pretrained,
          features_only=True,
          act_layer=act_layer,
          drop_rate=drop_rate,
          drop_path_rate=drop_path_rate,
      )
      if first_conv_stride_one:
          encoder.conv_stem.stride = (1, 1)
          encoder.feature_info = copy.deepcopy(encoder.feature_info)
          for fi in encoder.feature_info:
              fi["reduction"] = fi["reduction"] // 2
  
      super().__init__(encoder, layers)
  
file_path: pytorch-toolbelt/pytorch_toolbelt/modules/encoders/timm/efficient_net_v2.py
imports: import copy
  import math
  import warnings
  import torch
  from torch import nn
  from .common import GenericTimmEncoder
  from ..common import make_n_channel_input
  from ...activations import ACT_SILU, get_activation_block
label_desc: Use image node_id 1 to create a new TimmEfficientNetV2 object from inherited base classes: GenericTimmEncoder with example: obj = TimmEfficientNetV2(model_name, pretrained, layers, activation, drop_rate, drop_path_rate, first_conv_stride_one)