code: def forward(self, hidden_states):
      self_attention_outputs = self.attn(self.norm1(hidden_states))
      attention_output = self_attention_outputs[0]
      outputs = self_attention_outputs[1]
  
      # first residual connection
      hidden_states = self.drop_path(attention_output) + hidden_states
  
      # second residual connection is done here
      layer_output = hidden_states + self.drop_path(
          self.mlp(self.norm2(hidden_states))
      )
  
      outputs = (layer_output, outputs)
      return outputs
  
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 2 for calling the MgpstrLayer obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: outputs