class_docstr: The approximate form of Gaussian Error Linear Unit (GELU). For more details, see section 2:
  https://arxiv.org/abs/1606.08415.
  
  Parameters:
      dim_in (`int`): The number of channels in the input.
      dim_out (`int`): The number of channels in the output.
class_docstr_tok: ["The","approximate","form","of","Gaussian","Error","Linear","Unit","(","GELU",")",".","For","more","details",",","see","section","2",":","https",":","\/\/arxiv.org\/abs\/1606.08415",".","Parameters",":","dim_in","(","`","int","`",")",":","The","number","of","channels","in","the","input",".","dim_out","(","`","int","`",")",":","The","number","of","channels","in","the","output","."]
code: def __init__(self, dim_in: int, dim_out: int):
      super().__init__()
      self.proj = nn.Linear(dim_in, dim_out)
  
imports: from typing import Any, Dict, Optional, Tuple
  import torch
  import torch.nn.functional
  from torch import nn
  from ..utils import USE_PEFT_BACKEND
  from ..utils.torch_utils import maybe_allow_in_graph
  from .activations import get_activation
  from .attention_processor import Attention
  from .embeddings import CombinedTimestepLabelEmbeddings
  from .lora import LoRACompatibleLinear
label_desc: Use image node_id 1 to create a new ApproximateGELU object from inherited base classes: nn with example: obj = ApproximateGELU(dim_in, dim_out)