class_docstr: conv_c (uint)  --- c.
                      .`-- concat
                 .--a2
  conv_a (uint) `
                 `--a1 `-- add
  conv_b (int)  ---- b `
class_docstr_tok: ["conv_c","(","uint",")","--","-","c.",".","`","--","concat",".","--","a2","conv_a","(","uint",")","`","`","--","a1","`","--","add","conv_b","(","int",")","--","--","b","`"]
code: def __init__(self, activation_fn):
      super().__init__()
      self.conv_a = ConvBlock(activation_fn())
      self.conv_b = ConvBlock(nn.LeakyReLU())
      self.conv_c = ConvBlock(activation_fn())
  
imports: import operator
  from collections import OrderedDict
  from typing import List
  import pytest
  import torch
  import torch.ao.nn.quantized.reference
  import torch.ao.quantization
  import torch.nn
  import torch.nn.intrinsic
  import torch.nn.intrinsic.qat
  import torch.nn.qat
  import torch.nn.quantized
  from coremltools.optimize.torch.quantization import LinearQuantizer, LinearQuantizerConfig
  from coremltools.optimize.torch.quantization._backend_config import _mod_activations
  from coremltools.optimize.torch.quantization._qconfig_mapping import _QConfigMappingBuilder
  from coremltools.optimize.torch.quantization._utils import find_module, is_activation_post_process
  from coremltools.optimize.torch.quantization.modules import fused_modules
  from coremltools.optimize.torch.quantization.modules import qat_modules
  from coremltools.optimize.torch.quantization.modules import quantized_modules
  from coremltools.optimize.torch.quantization.quantization_config import QuantizationScheme
label_desc: Use image node_id 1 to create a new ComplexConcatAdd object from inherited base classes: nn with example: obj = ComplexConcatAdd(activation_fn)