docstr: Find the nearest children linear layers for the target node.
  
  Parameters
  ---------
  node : torch._C.Node
      target node.
  
  Returns
  -------
  children_layers: list
      nearest children linear layers for the target worknode.
docstr_tok: ["Find","the","nearest","children","linear","layers","for","the","target","node",".","Parameters","--","--","--","--","-","node",":","torch._C.Node","target","node",".","Returns","--","--","--","-","children_layers",":","list","nearest","children","linear","layers","for","the","target","worknode","."]
code: def _get_children_layers(self, node):
      """
      Find the nearest children linear layers for the target node.
  
      Parameters
      ---------
      node : torch._C.Node
          target node.
  
      Returns
      -------
      children_layers: list
          nearest children linear layers for the target worknode.
      """
      children_layers = []
      queue = []
      queue.append(node)
      while queue:
          curnode = queue.pop(0)
          if curnode.op_type == "Linear":
              if curnode.name not in children_layers:
                  children_layers.append(curnode.name)
              continue
          if curnode.op_type == "LayerNorm":
              continue
          children = self.graph.find_successors(curnode.unique_name)
          children = [
              self.graph.name_to_node[name] for name in children
          ]
          for child in children:
              queue.append(child)
      return children_layers
  
imports: import csv
  import logging
  import torch
  import numpy
  from .attr import get_nested_attr
label_desc: Use image node_id 3 for calling the AttentionWeightDependency obj's underlying member method code with example usage: obj._get_children_layers(node) and returns: children_layers