code: def __init__(
      self,
      pretrained: bool = True,
      drop_rate=0.0,
      drop_path_rate=0.1,
      activation=ACT_GELU,
      first_embedd_stride=4,
  ):
      super().__init__(
          embed_dims=[64, 128, 320, 512],
          num_heads=[1, 2, 5, 8],
          mlp_ratios=[4, 4, 4, 4],
          qkv_bias=True,
          norm_layer=partial(nn.LayerNorm, eps=1e-6),
          depths=[3, 4, 18, 3],
          sr_ratios=[8, 4, 2, 1],
          drop_rate=drop_rate,
          drop_path_rate=drop_path_rate,
          activation=activation,
          first_embedd_stride=first_embedd_stride,
      )
      if pretrained:
          checkpoint = torch.hub.load_state_dict_from_url(
              MitPretrainedWeights["mit_b3"],
              map_location="cpu",
              check_hash=True,
          )
          self.load_state_dict(checkpoint["model_state_dict"])
  
imports: import math
  from functools import partial
  from typing import Optional
  import torch
  import torch.nn
  from pytorch_toolbelt.modules.interfaces import AbstractEncoder, FeatureMapsSpecification
  from pytorch_toolbelt.modules.activations import instantiate_activation_block, ACT_GELU
  from pytorch_toolbelt.modules.drop_path import DropPath
label_desc: Use image node_id 1 to create a new MitB3Encoder object from inherited base classes: MixVisionTransformer with example: obj = MitB3Encoder(pretrained, drop_rate, drop_path_rate, activation, first_embedd_stride)