code: def forward(self, x: Tensor) -> Tensor:
      def attn_residual_func(x: Tensor) -> Tensor:
          return self.ls1(self.attn(self.norm1(x)))
  
      def ffn_residual_func(x: Tensor) -> Tensor:
          return self.ls2(self.mlp(self.norm2(x)))
  
      if self.training and self.sample_drop_ratio > 0.1:
          # the overhead is compensated only for a drop path rate larger than 0.1
          x = drop_add_residual_stochastic_depth(
              x,
              residual_func=attn_residual_func,
              sample_drop_ratio=self.sample_drop_ratio,
          )
          x = drop_add_residual_stochastic_depth(
              x,
              residual_func=ffn_residual_func,
              sample_drop_ratio=self.sample_drop_ratio,
          )
      elif self.training and self.sample_drop_ratio > 0.0:
          x = x + self.drop_path1(attn_residual_func(x))
          x = x + self.drop_path1(
              ffn_residual_func(x)
          )  # FIXME: drop_path2
      else:
          x = x + attn_residual_func(x)
          x = x + ffn_residual_func(x)
      return x
  
imports: import logging
  import os
  from typing import Callable, List, Any, Tuple, Dict
  import warnings
  import torch
  from torch import nn, Tensor
  from .attention import Attention, MemEffAttention
  from .drop_path import DropPath
  from .layer_scale import LayerScale
  from .mlp import Mlp
label_desc: Use image node_id 2 for calling the Block obj's underlying member method code with example usage: obj.forward(x) and returns: x, self, self