class_docstr: Transformer MLP block.
class_docstr_tok: ["Transformer","MLP","block","."]
code: def __init__(self, in_dim: int, mlp_dim: int, dropout: float):
      super().__init__(
          in_dim,
          [mlp_dim, in_dim],
          activation_layer=nn.GELU,
          inplace=None,
          dropout=dropout,
      )
  
      for m in self.modules():
          if isinstance(m, nn.Linear):
              nn.init.xavier_uniform_(m.weight)
              if m.bias is not None:
                  nn.init.normal_(m.bias, std=1e-6)
  
imports: import math
  from collections import OrderedDict
  from functools import partial
  from typing import Any, Callable, Dict, List, NamedTuple, Optional
  import torch
  import torch.nn
  from ..ops.misc import Conv2dNormActivation, MLP
  from ..transforms._presets import ImageClassification, InterpolationMode
  from ..utils import _log_api_usage_once
  from ._api import register_model, Weights, WeightsEnum
  from ._meta import _IMAGENET_CATEGORIES
  from ._utils import _ovewrite_named_param, handle_legacy_interface
label_desc: Use image node_id 1 to create a new MLPBlock object from inherited base classes: MLP with example: obj = MLPBlock(in_dim, mlp_dim, dropout)