code: def __init__(self, config: MgpstrConfig, drop_path=None):
      super().__init__()
      self.norm1 = nn.LayerNorm(
          config.hidden_size, eps=config.layer_norm_eps
      )
      self.attn = MgpstrAttention(config)
      # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
      self.drop_path = (
          MgpstrDropPath(drop_path)
          if drop_path is not None
          else nn.Identity()
      )
      self.norm2 = nn.LayerNorm(
          config.hidden_size, eps=config.layer_norm_eps
      )
      mlp_hidden_dim = int(config.hidden_size * config.mlp_ratio)
      self.mlp = MgpstrMlp(config, mlp_hidden_dim)
  
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 MgpstrLayer object from inherited base classes: nn with example: obj = MgpstrLayer(config, drop_path)