class_docstr: BlazeFace, see https://arxiv.org/abs/1907.05047
  
  Args:
      blaze_filters (list): number of filter for each blaze block.
      double_blaze_filters (list): number of filter for each double_blaze block.
      use_5x5kernel (bool): whether or not filter size is 5x5 in depth-wise conv.
class_docstr_tok: ["BlazeFace",",","see","https",":","\/\/arxiv.org\/abs\/1907.05047","Args",":","blaze_filters","(","list",")",":","number","of","filter","for","each","blaze","block",".","double_blaze_filters","(","list",")",":","number","of","filter","for","each","double_blaze","block",".","use_5x5kernel","(","bool",")",":","whether","or","not","filter","size","is","5x5","in","depth-wise","conv","."]
code: def forward(self, inputs):
      outs = []
      y = self.conv1(inputs["image"])
      for block in self.blaze_block:
          y = block(y)
          outs.append(y)
      return [outs[-4], outs[-1]]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.nn.initializer import KaimingNormal
  from ppdet.core.workspace import register, serializable
  from ..shape_spec import ShapeSpec
label_desc: Use image node_id 2 for calling the BlazeNet obj's underlying member method code with example usage: obj.forward(inputs) and returns: list