class_docstr: A `LightAdapterResnetBlock` is a helper model that implements a ResNet-like block with a slightly different
  architecture than `AdapterResnetBlock`.
  
  Parameters:
      channels (`int`):
          Number of channels of LightAdapterResnetBlock's input and output.
class_docstr_tok: ["A","`","LightAdapterResnetBlock","`","is","a","helper","model","that","implements","a","ResNet-like","block","with","a","slightly","different","architecture","than","`","AdapterResnetBlock","`",".","Parameters",":","channels","(","`","int","`",")",":","Number","of","channels","of","LightAdapterResnetBlock","'s","input","and","output","."]
docstr: This function takes input tensor x and processes it through one convolutional layer, ReLU activation, and
  another convolutional layer and adds it to input tensor.
docstr_tok: ["This","function","takes","input","tensor","x","and","processes","it","through","one","convolutional","layer",",","ReLU","activation",",","and","another","convolutional","layer","and","adds","it","to","input","tensor","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      r"""
      This function takes input tensor x and processes it through one convolutional layer, ReLU activation, and
      another convolutional layer and adds it to input tensor.
      """
  
      h = self.act(self.block1(x))
      h = self.block2(h)
  
      return h + x
  
imports: import os
  from typing import Callable, List, Optional, Union
  import torch
  import torch.nn
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import logging
  from .modeling_utils import ModelMixin
label_desc: Use image node_id 2 for calling the LightAdapterResnetBlock obj's underlying member method code with example usage: obj.forward(x) and returns: unknown