code: def forward(self, conditioning):
      embedding = self.conv_in(conditioning)
      embedding = F.silu(embedding)
  
      for block in self.blocks:
          embedding = block(embedding)
          embedding = F.silu(embedding)
  
      embedding = self.conv_out(embedding)
  
      return embedding
  
file_path: ml-stable-diffusion/python_coreml_stable_diffusion/controlnet.py
imports: from diffusers.configuration_utils import ConfigMixin, register_to_config
  from diffusers import ModelMixin
  import torch
  import torch.nn
  import torch.nn.functional
  from .unet import Timesteps, TimestepEmbedding, get_down_block, UNetMidBlock2DCrossAttn, linear_to_conv2d_map
label_desc: Use image node_id 2 for calling the ControlNetConditioningEmbedding obj's underlying member method code with example usage: obj.forward(conditioning) and returns: embedding