code: def __init__(
      self,
      encoder,
      features,
      layers=[1, 2, 3, 4],
      first_conv_stride_one: bool = False,
  ):
      strides = [2, 4, 8, 16, 32]
  
      if first_conv_stride_one:
          strides = [1, 2, 4, 8, 16]
          encoder.conv_stem.stride = (1, 1)
  
      # if no_stride_s16:
      #     encoder.blocks[3][0].conv_dw.stride = (1, 1)
      #     encoder.blocks[3][0].conv_dw.dilation = (2, 2)
      #     strides[3] = strides[2]
      #     strides[4] = strides[3] * 2
      #
      # if no_stride_s32:
      #     encoder.blocks[5][0].conv_dw.stride = (1, 1)
      #     encoder.blocks[5][0].conv_dw.dilation = (2, 2)
      #     strides[4] = strides[3]
  
      super().__init__(features, strides, layers)
      self.encoder = encoder
  
imports: import math
  import warnings
  import torch
  from torch import nn
  from ..common import EncoderModule, _take
  from ...activations import ACT_SILU, get_activation_block
label_desc: Use image node_id 1 to create a new TimmBaseEfficientNetEncoder object from inherited base classes: EncoderModule with example: obj = TimmBaseEfficientNetEncoder(encoder, features, layers, first_conv_stride_one)