code: def __init__(self, config: MgpstrConfig):
      super().__init__()
      self.token_norm = nn.LayerNorm(
          config.hidden_size, eps=config.layer_norm_eps
      )
      self.tokenLearner = nn.Sequential(
          nn.Conv2d(
              config.hidden_size,
              config.hidden_size,
              kernel_size=(1, 1),
              stride=1,
              groups=8,
              bias=False,
          ),
          nn.Conv2d(
              config.hidden_size,
              config.max_token_length,
              kernel_size=(1, 1),
              stride=1,
              bias=False,
          ),
      )
      self.feat = nn.Conv2d(
          config.hidden_size,
          config.hidden_size,
          kernel_size=(1, 1),
          stride=1,
          groups=8,
          bias=False,
      )
      self.norm = nn.LayerNorm(
          config.hidden_size, eps=config.layer_norm_eps
      )
  
imports: import collections.abc
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch import nn
  from ...modeling_outputs import BaseModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_mgp_str import MgpstrConfig
label_desc: Use image node_id 1 to create a new MgpstrA3Module object from inherited base classes: nn with example: obj = MgpstrA3Module(config)