class_docstr: The weighted averaging of cell operations.
  Args:
      c: number of output channels.
      ops: a dictionary of operations. See also: ``Cell.OPS2D`` or ``Cell.OPS3D``.
      arch_code_c: binary cell operation code. It represents the operation results added to the output.
class_docstr_tok: ["The","weighted","averaging","of","cell","operations",".","Args",":","c",":","number","of","output","channels",".","ops",":","a","dictionary","of","operations",".","See","also",":","``","Cell.OPS2D","``","or","``","Cell.OPS3D","``",".","arch_code_c",":","binary","cell","operation","code",".","It","represents","the","operation","results","added","to","the","output","."]
code: def __init__(self, c: int, ops: dict, arch_code_c=None):
      super().__init__()
      if arch_code_c is None:
          arch_code_c = np.ones(len(ops))
      self.ops = nn.ModuleList()
      for arch_c, op_name in zip(arch_code_c, ops):
          if arch_c > 0:
              self.ops.append(ops[op_name](c))
  
imports: from __future__ import annotations
  import datetime
  import warnings
  from typing import Optional
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from monai.networks.blocks.dints_block import ActiConvNormBlock, FactorizedIncreaseBlock, FactorizedReduceBlock, P3DActiConvNormBlock
  from monai.networks.layers.factories import Conv
  from monai.networks.layers.utils import get_act_layer, get_norm_layer
  from monai.utils import optional_import
label_desc: Use image node_id 1 to create a new MixedOp object from inherited base classes: nn with example: obj = MixedOp(c, ops, arch_code_c)