class_docstr: Single operation for sampled path.
class_docstr_tok: ["Single","operation","for","sampled","path","."]
docstr: Parameters
  ----------
  layers_configs : list
      the layer config: [input_channel, output_channel, stride, height]
  stage_ops : dict
      the pairs of op name and layer operator
  sampled_op : str
      the searched layer name
docstr_tok: ["Parameters","--","--","--","--","--","layers_configs",":","list","the","layer","config",":","[","input_channel",",","output_channel",",","stride",",","height","]","stage_ops",":","dict","the","pairs","of","op","name","and","layer","operator","sampled_op",":","str","the","searched","layer","name"]
code: def __init__(self, layers_configs, stage_ops, sampled_op=""):
      """
      Parameters
      ----------
      layers_configs : list
          the layer config: [input_channel, output_channel, stride, height]
      stage_ops : dict
          the pairs of op name and layer operator
      sampled_op : str
          the searched layer name
      """
      super(SingleOperation, self).__init__()
      fm = {"fm_size": layers_configs[3]}
      ops_names = [op_name for op_name in stage_ops]
      sampled_op = sampled_op if sampled_op else ops_names[0]
  
      # define the single op
      self.op = stage_ops[sampled_op](*layers_configs[0:3], **fm)
  
imports: from __future__ import absolute_import, division, print_function
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new SingleOperation object from inherited base classes: nn with example: obj = SingleOperation(layers_configs, stage_ops, sampled_op)