code: def __init__(self, pretrained=True, layers=None, activation=ACT_RELU):
      if layers is None:
          layers = [1, 2, 3, 4]
      from timm.models import tresnet_m
  
      act_layer = get_activation_block(activation)
      encoder = tresnet_m(pretrained=pretrained, act_layer=act_layer)
  
      super().__init__(
          [64, 64, 128, 1024, 2048], [4, 4, 8, 16, 32], layers
      )
      self.stem = nn.Sequential(
          encoder.body.SpaceToDepth, encoder.body.conv1
      )
  
      self.layer1 = encoder.body.layer1
      self.layer2 = encoder.body.layer2
      self.layer3 = encoder.body.layer3
      self.layer4 = encoder.body.layer4
  
imports: from collections import OrderedDict
  from typing import List
  import torch
  from torch import nn
  from .common import GenericTimmEncoder
  from ..common import EncoderModule, make_n_channel_input
  from ...None import ACT_RELU, get_activation_block
label_desc: Use image node_id 1 to create a new TResNetMEncoder object from inherited base classes: EncoderModule with example: obj = TResNetMEncoder(pretrained, layers, activation)