<
   ir_version: 10,
   opset_import: ["" : 20],
   metadata_props: ["character": "0
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"]
>
"PaddlePaddle Graph in PIR mode" (uint8[batch,48,width,3] image) => (int32[batch,seq] ctc_indices, float[batch,seq] ctc_confidence) 
   <
      float[batch,240,12,unk__11] "Add.105"
      float[batch,240,12,unk__11] "Add.111"
      float[batch,240,12,unk__11] "Add.117"
      float[batch,240,6,unk__11] "Add.123"
      float[batch,240,6,unk__11] "Add.125"
      float[batch,60,1,1] "Add.127"
      float[batch,240,1,1] "Add.129"
      float[batch,480,6,unk__11] "Add.133"
      float[batch,480,6,unk__11] "Add.139"
      float[batch,480,6,unk__11] "Add.141"
      float[batch,120,1,1] "Add.143"
      float[batch,480,1,1] "Add.145"
      float[batch,480,6,unk__11] "Add.149"
      float[batch,32,24,unk__0] "Add.15"
      float[batch,480,3,unk__11] "Add.155"
      float[batch,480,3,unk__11] "Add.161"
      float[batch,480,3,unk__11] "Add.167"
      float[batch,480,3,unk__11] "Add.173"
      float[batch,seq,120] "Add.181"
      float[batch,seq,360] "Add.183"
      float[batch,8,seq,15] "Add.183_k"
      float[batch,8,seq,15] "Add.183_q"
      float[batch,seq,24,15] "Add.183_qkv"
      float[batch,24,seq,15] "Add.183_qkv_heads"
      float[batch,8,seq,15] "Add.183_v"
      float[batch,seq,120] "Add.185"
      float[batch,seq,120] "Add.187"
      float[batch,seq,120] "Add.191"
      float[batch,seq,240] "Add.193"
      float[batch,seq,120] "Add.195"
      float[batch,seq,120] "Add.197"
      float[batch,seq,120] "Add.201"
      float[batch,seq,360] "Add.203"
      float[batch,8,seq,15] "Add.203_k"
      float[batch,8,seq,15] "Add.203_q"
      float[batch,seq,24,15] "Add.203_qkv"
      float[batch,24,seq,15] "Add.203_qkv_heads"
      float[batch,8,seq,15] "Add.203_v"
      float[batch,seq,120] "Add.205"
      float[batch,seq,120] "Add.207"
      float[batch,64,24,unk__0] "Add.21"
      float[batch,seq,120] "Add.211"
      float[batch,seq,240] "Add.213"
      float[batch,seq,120] "Add.215"
      float[batch,seq,120] "Add.217"
      float[batch,seq,120] "Add.221"
      float[batch,seq,438] "Add.223"
      float[batch,64,24,unk__0] "Add.27"
      float[batch,16,24,unk__0] "Add.3"
      float[batch,64,24,unk__0] "Add.33"
      float[batch,64,12,unk__0] "Add.39"
      float[batch,128,12,unk__0] "Add.45"
      float[batch,128,12,unk__0] "Add.51"
      float[batch,128,12,unk__0] "Add.57"
      float[batch,128,12,unk__11] "Add.63"
      float[batch,240,12,unk__11] "Add.69"
      float[batch,240,12,unk__11] "Add.75"
      float[batch,240,12,unk__11] "Add.81"
      float[batch,240,12,unk__11] "Add.87"
      float[batch,32,24,unk__0] "Add.9"
      float[batch,240,12,unk__11] "Add.93"
      float[batch,240,12,unk__11] "Add.99"
      float[batch,960,1,seq] "Concat.4"
      float[batch,240,6,unk__11] "Mul.83"
      float[batch,240,6,unk__11] "Mul.85"
      float[batch,480,6,unk__11] "Mul.93"
      float[batch,480,6,unk__11] "Mul.95"
      float[batch,60,1,seq] "Sigmoid.1"
      float[batch,60,1,seq] "Sigmoid.11"
      float[batch,120,1,seq] "Sigmoid.13"
      float[batch,120,1,seq] "Sigmoid.3"
      float[batch,seq,240] "Sigmoid.5"
      float[batch,seq,240] "Sigmoid.7"
      float[batch,480,1,seq] "Sigmoid.9"
      float[batch,16,24,unk__0] "p2o.pd_op.batch_norm_.0.0"
      float[batch,60,1,seq] "p2o.pd_op.batch_norm_.1.0"
      float[batch,120,1,seq] "p2o.pd_op.batch_norm_.2.0"
      float[batch,480,1,seq] "p2o.pd_op.batch_norm_.3.0"
      float[batch,60,1,seq] "p2o.pd_op.batch_norm_.4.0"
      float[batch,120,1,seq] "p2o.pd_op.batch_norm_.5.0"
      float[batch,120,seq] "p2o.pd_op.flatten.0.0"
      float[batch,240,1,1] "p2o.pd_op.hardsigmoid.0.0"
      float[batch,480,1,1] "p2o.pd_op.hardsigmoid.1.0"
      float[batch,16,24,unk__0] "p2o.pd_op.hardswish.0.0"
      float[batch,32,24,unk__0] "p2o.pd_op.hardswish.1.0"
      float[batch,128,12,unk__11] "p2o.pd_op.hardswish.10.0"
      float[batch,240,12,unk__11] "p2o.pd_op.hardswish.11.0"
      float[batch,240,12,unk__11] "p2o.pd_op.hardswish.12.0"
      float[batch,240,12,unk__11] "p2o.pd_op.hardswish.13.0"
      float[batch,240,12,unk__11] "p2o.pd_op.hardswish.14.0"
      float[batch,240,12,unk__11] "p2o.pd_op.hardswish.15.0"
      float[batch,240,12,unk__11] "p2o.pd_op.hardswish.16.0"
      float[batch,240,12,unk__11] "p2o.pd_op.hardswish.17.0"
      float[batch,240,12,unk__11] "p2o.pd_op.hardswish.18.0"
      float[batch,240,12,unk__11] "p2o.pd_op.hardswish.19.0"
      float[batch,32,24,unk__0] "p2o.pd_op.hardswish.2.0"
      float[batch,240,6,unk__11] "p2o.pd_op.hardswish.20.0"
      float[batch,480,6,unk__11] "p2o.pd_op.hardswish.21.0"
      float[batch,480,6,unk__11] "p2o.pd_op.hardswish.22.0"
      float[batch,480,6,unk__11] "p2o.pd_op.hardswish.23.0"
      float[batch,480,3,unk__11] "p2o.pd_op.hardswish.24.0"
      float[batch,480,3,unk__11] "p2o.pd_op.hardswish.25.0"
      float[batch,480,3,unk__11] "p2o.pd_op.hardswish.26.0"
      float[batch,480,3,unk__11] "p2o.pd_op.hardswish.27.0"
      float[batch,64,24,unk__0] "p2o.pd_op.hardswish.3.0"
      float[batch,64,24,unk__0] "p2o.pd_op.hardswish.4.0"
      float[batch,64,24,unk__0] "p2o.pd_op.hardswish.5.0"
      float[batch,64,12,unk__0] "p2o.pd_op.hardswish.6.0"
      float[batch,128,12,unk__0] "p2o.pd_op.hardswish.7.0"
      float[batch,128,12,unk__0] "p2o.pd_op.hardswish.8.0"
      float[batch,128,12,unk__0] "p2o.pd_op.hardswish.9.0"
      float[batch,seq,360] "p2o.pd_op.matmul.0.0"
      float[batch,8,seq,seq] "p2o.pd_op.matmul.1.0"
      float[batch,seq,240] "p2o.pd_op.matmul.10.0"
      float[batch,seq,120] "p2o.pd_op.matmul.11.0"
      float[batch,seq,438] "p2o.pd_op.matmul.12.0"
      float[batch,8,seq,15] "p2o.pd_op.matmul.2.0"
      float[batch,seq,120] "p2o.pd_op.matmul.3.0"
      float[batch,seq,240] "p2o.pd_op.matmul.4.0"
      float[batch,seq,120] "p2o.pd_op.matmul.5.0"
      float[batch,seq,360] "p2o.pd_op.matmul.6.0"
      float[batch,8,seq,seq] "p2o.pd_op.matmul.7.0"
      float[batch,8,seq,15] "p2o.pd_op.matmul.8.0"
      float[batch,seq,120] "p2o.pd_op.matmul.9.0"
      float[batch,240,1,1] "p2o.pd_op.pool2d.0.0"
      float[batch,480,1,1] "p2o.pd_op.pool2d.1.0"
      float[batch,480,1,seq] "p2o.pd_op.pool2d.2.0"
      float[batch,60,1,1] "p2o.pd_op.relu.0.0"
      float[batch,120,1,1] "p2o.pd_op.relu.1.0"
      float[batch,seq,120] "p2o.pd_op.reshape.33.0"
      float[batch,seq,120] "p2o.pd_op.reshape.35.0"
      float[batch,1,seq,120] "p2o.pd_op.reshape.36.0"
      float[batch,8,seq,seq] "p2o.pd_op.softmax.0.0"
      float[batch,8,seq,seq] "p2o.pd_op.softmax.1.0"
      float[batch,120,seq] "p2o.pd_op.squeeze.0.0"
      float[batch,60,1,seq] "p2o.pd_op.swish.0.0"
      float[batch,120,1,seq] "p2o.pd_op.swish.1.0"
      float[batch,seq,240] "p2o.pd_op.swish.2.0"
      float[batch,seq,240] "p2o.pd_op.swish.3.0"
      float[batch,480,1,seq] "p2o.pd_op.swish.4.0"
      float[batch,60,1,seq] "p2o.pd_op.swish.5.0"
      float[batch,120,1,seq] "p2o.pd_op.swish.6.0"
      float[batch,seq,120] "p2o.pd_op.transpose.0.0"
      float[batch,8,15,seq] "p2o.pd_op.transpose.2.0"
      float[batch,seq,8,15] "p2o.pd_op.transpose.3.0"
      float[batch,8,15,seq] "p2o.pd_op.transpose.5.0"
      float[batch,seq,8,15] "p2o.pd_op.transpose.6.0"
      float[batch,120,1,seq] "p2o.pd_op.transpose.7.0"
      float[batch,seq,120] "p2o.pd_op.transpose.8.0"
      float[batch,seq,1] max_logits
      float[batch,48,width,3] tmp
      float[batch,3,48,width] tmp_0
      float[batch,seq,438] tmp_0_2
      float[batch,seq,438] tmp_1
      int64[batch,seq] tmp_2
      float[batch,seq] tmp_3
      float[batch,16,24,unk__0] val_0
      float[batch,32,24,unk__0] val_1
      float[batch,128,12,unk__0] val_10
      float[batch,128,12,unk__0] val_11
      float[batch,128,12,unk__0] val_12
      float[batch,128,12,unk__0] val_13
      float[batch,128,12,unk__0] val_14
      float[batch,128,12,unk__11] val_15
      float[batch,240,12,unk__11] val_16
      float[batch,240,12,unk__11] val_17
      float[batch,240,12,unk__11] val_18
      float[batch,240,12,unk__11] val_19
      float[batch,32,24,unk__0] val_2
      float[batch,240,12,unk__11] val_20
      float[batch,240,12,unk__11] val_21
      float[batch,240,12,unk__11] val_22
      float[batch,240,12,unk__11] val_23
      float[batch,240,12,unk__11] val_24
      float[batch,240,12,unk__11] val_25
      float[batch,240,12,unk__11] val_26
      float[batch,240,12,unk__11] val_27
      float[batch,240,12,unk__11] val_28
      float[batch,240,12,unk__11] val_29
      float[batch,32,24,unk__0] val_3
      float[batch,240,6,unk__11] val_30
      float[batch,480,6,unk__11] val_31
      float[batch,480,6,unk__11] val_32
      float[batch,480,6,unk__11] val_33
      float[batch,480,6,unk__11] val_34
      float[batch,480,6,unk__11] val_35
      float[batch,480,3,unk__11] val_36
      float[batch,480,3,unk__11] val_37
      float[batch,480,3,unk__11] val_38
      float[batch,480,3,unk__11] val_39
      float[batch,64,24,unk__0] val_4
      float[batch,480,3,unk__11] val_40
      float[batch,480,1,seq] val_41
      float[batch,480,1,seq] val_42
      float[batch,64,24,unk__0] val_5
      float[batch,64,24,unk__0] val_6
      float[batch,64,24,unk__0] val_7
      float[batch,64,24,unk__0] val_8
      float[batch,64,12,unk__0] val_9
      float[batch,3,48,width] x
   >
{
   [n0] tmp = Cast <to: int = 1> (image)
   [n1] tmp_0 = Transpose <perm: ints = [0, 3, 1, 2]> (tmp)
   [n4] x = Sub (tmp_0, const_cast)
   "p2o.pd_op.batch_norm_.0.0" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [2, 2]> (x, "conv2d_0.w_0", "conv2d_0.w_0_bias")
   "Add.3" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 16, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("p2o.pd_op.batch_norm_.0.0", "conv2d_136.w_0_lab", "p2o.pd_op.depthwise_conv2d.0.0_bias_lab")
   val_0 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.3")
   "p2o.pd_op.hardswish.0.0" = Mul ("Add.3", val_0)
   "Add.9" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.hardswish.0.0", "conv2d_137.w_0_lab_lab", "p2o.pd_op.conv2d.1.0_bias_lab_lab")
   val_1 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.9")
   "p2o.pd_op.hardswish.1.0" = Mul ("Add.9", val_1)
   val_2 = Add ("p2o.pd_op.hardswish.1.0", "conv2d_138.w_0_lab_laboffset")
   "Add.15" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 32, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (val_2, "conv2d_138.w_0_lab_labscale", "p2o.pd_op.depthwise_conv2d.1.0_bias_lab")
   val_3 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.15")
   "p2o.pd_op.hardswish.2.0" = Mul ("Add.15", val_3)
   "Add.21" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.hardswish.2.0", "conv2d_139.w_0_lab_lab", "p2o.pd_op.conv2d.2.0_bias_lab_lab")
   val_4 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.21")
   "p2o.pd_op.hardswish.3.0" = Mul ("Add.21", val_4)
   val_5 = Add ("p2o.pd_op.hardswish.3.0", "conv2d_140.w_0_lab_laboffset")
   "Add.27" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 64, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (val_5, "conv2d_140.w_0_lab_labscale", "p2o.pd_op.depthwise_conv2d.2.0_bias_lab")
   val_6 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.27")
   "p2o.pd_op.hardswish.4.0" = Mul ("Add.27", val_6)
   "Add.33" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.hardswish.4.0", "conv2d_141.w_0_lab_lab", "p2o.pd_op.conv2d.3.0_bias_lab_lab")
   val_7 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.33")
   "p2o.pd_op.hardswish.5.0" = Mul ("Add.33", val_7)
   val_8 = Add ("p2o.pd_op.hardswish.5.0", "conv2d_142.w_0_lab_laboffset")
   "Add.39" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 64, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [2, 1]> (val_8, "conv2d_142.w_0_lab_labscale", "p2o.pd_op.depthwise_conv2d.3.0_bias_lab")
   val_9 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.39")
   "p2o.pd_op.hardswish.6.0" = Mul ("Add.39", val_9)
   "Add.45" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.hardswish.6.0", "conv2d_143.w_0_lab_lab", "p2o.pd_op.conv2d.4.0_bias_lab_lab")
   val_10 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.45")
   "p2o.pd_op.hardswish.7.0" = Mul ("Add.45", val_10)
   val_11 = Add ("p2o.pd_op.hardswish.7.0", "conv2d_144.w_0_lab_laboffset")
   "Add.51" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 128, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (val_11, "conv2d_144.w_0_lab_labscale", "p2o.pd_op.depthwise_conv2d.4.0_bias_lab")
   val_12 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.51")
   "p2o.pd_op.hardswish.8.0" = Mul ("Add.51", val_12)
   "Add.57" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.hardswish.8.0", "conv2d_145.w_0_lab_lab", "p2o.pd_op.conv2d.5.0_bias_lab_lab")
   val_13 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.57")
   "p2o.pd_op.hardswish.9.0" = Mul ("Add.57", val_13)
   val_14 = Add ("p2o.pd_op.hardswish.9.0", "conv2d_146.w_0_lab_laboffset")
   "Add.63" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 128, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 2]> (val_14, "conv2d_146.w_0_lab_labscale", "p2o.pd_op.depthwise_conv2d.5.0_bias_lab")
   val_15 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.63")
   "p2o.pd_op.hardswish.10.0" = Mul ("Add.63", val_15)
   "Add.69" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.hardswish.10.0", "conv2d_147.w_0_lab_lab", "p2o.pd_op.conv2d.6.0_bias_lab_lab")
   val_16 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.69")
   "p2o.pd_op.hardswish.11.0" = Mul ("Add.69", val_16)
   val_17 = Add ("p2o.pd_op.hardswish.11.0", "conv2d_148.w_0_lab_laboffset")
   "Add.75" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 240, kernel_shape: ints = [5, 5], pads: ints = [2, 2, 2, 2], strides: ints = [1, 1]> (val_17, "conv2d_148.w_0_lab_labscale", "p2o.pd_op.depthwise_conv2d.6.0_bias_lab")
   val_18 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.75")
   "p2o.pd_op.hardswish.12.0" = Mul ("Add.75", val_18)
   "Add.81" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.hardswish.12.0", "conv2d_149.w_0_lab_lab", "p2o.pd_op.conv2d.7.0_bias_lab_lab")
   val_19 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.81")
   "p2o.pd_op.hardswish.13.0" = Mul ("Add.81", val_19)
   val_20 = Add ("p2o.pd_op.hardswish.13.0", "conv2d_150.w_0_lab_laboffset")
   "Add.87" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 240, kernel_shape: ints = [5, 5], pads: ints = [2, 2, 2, 2], strides: ints = [1, 1]> (val_20, "conv2d_150.w_0_lab_labscale", "p2o.pd_op.depthwise_conv2d.7.0_bias_lab")
   val_21 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.87")
   "p2o.pd_op.hardswish.14.0" = Mul ("Add.87", val_21)
   "Add.93" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.hardswish.14.0", "conv2d_151.w_0_lab_lab", "p2o.pd_op.conv2d.8.0_bias_lab_lab")
   val_22 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.93")
   "p2o.pd_op.hardswish.15.0" = Mul ("Add.93", val_22)
   val_23 = Add ("p2o.pd_op.hardswish.15.0", "conv2d_152.w_0_lab_laboffset")
   "Add.99" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 240, kernel_shape: ints = [5, 5], pads: ints = [2, 2, 2, 2], strides: ints = [1, 1]> (val_23, "conv2d_152.w_0_lab_labscale", "p2o.pd_op.depthwise_conv2d.8.0_bias_lab")
   val_24 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.99")
   "p2o.pd_op.hardswish.16.0" = Mul ("Add.99", val_24)
   "Add.105" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.hardswish.16.0", "conv2d_153.w_0_lab_lab", "p2o.pd_op.conv2d.9.0_bias_lab_lab")
   val_25 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.105")
   "p2o.pd_op.hardswish.17.0" = Mul ("Add.105", val_25)
   val_26 = Add ("p2o.pd_op.hardswish.17.0", "conv2d_154.w_0_lab_laboffset")
   "Add.111" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 240, kernel_shape: ints = [5, 5], pads: ints = [2, 2, 2, 2], strides: ints = [1, 1]> (val_26, "conv2d_154.w_0_lab_labscale", "p2o.pd_op.depthwise_conv2d.9.0_bias_lab")
   val_27 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.111")
   "p2o.pd_op.hardswish.18.0" = Mul ("Add.111", val_27)
   "Add.117" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.hardswish.18.0", "conv2d_155.w_0_lab_lab", "p2o.pd_op.conv2d.10.0_bias_lab_lab")
   val_28 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.117")
   "p2o.pd_op.hardswish.19.0" = Mul ("Add.117", val_28)
   val_29 = Add ("p2o.pd_op.hardswish.19.0", "conv2d_156.w_0_lab_laboffset")
   "Add.123" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 240, kernel_shape: ints = [5, 5], pads: ints = [2, 2, 2, 2], strides: ints = [2, 1]> (val_29, "conv2d_156.w_0_lab_labscale", "p2o.pd_op.depthwise_conv2d.10.0_bias_lab")
   val_30 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.123")
   "p2o.pd_op.hardswish.20.0" = Mul ("Add.123", val_30)
   ["Mul.82"] "Mul.83" = Mul ("learnable_affine_block_41.w_0", "p2o.pd_op.hardswish.20.0")
   ["Add.124"] "Add.125" = Add ("Mul.83", "learnable_affine_block_41.w_1")
   ["GlobalAveragePool.0"] "p2o.pd_op.pool2d.0.0" = GlobalAveragePool ("Add.125")
   "Add.127" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.pool2d.0.0", "conv2d_96.w_0", "p2o.pd_op.conv2d.11.0_bias")
   ["Relu.0"] "p2o.pd_op.relu.0.0" = Relu ("Add.127")
   "Add.129" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.relu.0.0", "conv2d_97.w_0", "p2o.pd_op.conv2d.12.0_bias")
   ["HardSigmoid.0"] "p2o.pd_op.hardsigmoid.0.0" = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.129")
   ["Mul.84"] "Mul.85" = Mul ("Add.125", "p2o.pd_op.hardsigmoid.0.0")
   "Add.133" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Mul.85", "conv2d_157.w_0_lab", "p2o.pd_op.conv2d.13.0_bias_lab")
   val_31 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.133")
   "p2o.pd_op.hardswish.21.0" = Mul ("Add.133", val_31)
   val_32 = Add ("p2o.pd_op.hardswish.21.0", "conv2d_158.w_0_lab_laboffset")
   "Add.139" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 480, kernel_shape: ints = [5, 5], pads: ints = [2, 2, 2, 2], strides: ints = [1, 1]> (val_32, "conv2d_158.w_0_lab_labscale", "p2o.pd_op.depthwise_conv2d.11.0_bias_lab")
   val_33 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.139")
   "p2o.pd_op.hardswish.22.0" = Mul ("Add.139", val_33)
   ["Mul.92"] "Mul.93" = Mul ("learnable_affine_block_45.w_0", "p2o.pd_op.hardswish.22.0")
   ["Add.140"] "Add.141" = Add ("Mul.93", "learnable_affine_block_45.w_1")
   ["GlobalAveragePool.1"] "p2o.pd_op.pool2d.1.0" = GlobalAveragePool ("Add.141")
   "Add.143" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.pool2d.1.0", "conv2d_107.w_0", "p2o.pd_op.conv2d.14.0_bias")
   ["Relu.1"] "p2o.pd_op.relu.1.0" = Relu ("Add.143")
   "Add.145" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.relu.1.0", "conv2d_108.w_0", "p2o.pd_op.conv2d.15.0_bias")
   ["HardSigmoid.1"] "p2o.pd_op.hardsigmoid.1.0" = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.145")
   ["Mul.94"] "Mul.95" = Mul ("Add.141", "p2o.pd_op.hardsigmoid.1.0")
   "Add.149" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("Mul.95", "conv2d_159.w_0_lab", "p2o.pd_op.conv2d.16.0_bias_lab")
   val_34 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.149")
   "p2o.pd_op.hardswish.23.0" = Mul ("Add.149", val_34)
   val_35 = Add ("p2o.pd_op.hardswish.23.0", "conv2d_160.w_0_lab_laboffset")
   "Add.155" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 480, kernel_shape: ints = [5, 5], pads: ints = [2, 2, 2, 2], strides: ints = [2, 1]> (val_35, "conv2d_160.w_0_lab_labscale", "p2o.pd_op.depthwise_conv2d.12.0_bias_lab")
   val_36 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.155")
   "p2o.pd_op.hardswish.24.0" = Mul ("Add.155", val_36)
   "Add.161" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.hardswish.24.0", "conv2d_161.w_0_lab_lab", "p2o.pd_op.conv2d.17.0_bias_lab_lab")
   val_37 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.161")
   "p2o.pd_op.hardswish.25.0" = Mul ("Add.161", val_37)
   val_38 = Add ("p2o.pd_op.hardswish.25.0", "conv2d_162.w_0_lab_laboffset")
   "Add.167" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 480, kernel_shape: ints = [5, 5], pads: ints = [2, 2, 2, 2], strides: ints = [1, 1]> (val_38, "conv2d_162.w_0_lab_labscale", "p2o.pd_op.depthwise_conv2d.13.0_bias_lab")
   val_39 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.167")
   "p2o.pd_op.hardswish.26.0" = Mul ("Add.167", val_39)
   "Add.173" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.hardswish.26.0", "conv2d_163.w_0_lab_lab", "p2o.pd_op.conv2d.18.0_bias_lab_lab")
   val_40 = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> ("Add.173")
   "p2o.pd_op.hardswish.27.0" = Mul ("Add.173", val_40)
   val_41 = AveragePool <auto_pad: string = "NOTSET", ceil_mode: int = 0, count_include_pad: int = 0, kernel_shape: ints = [3, 2], pads: ints = [0, 0, 0, 0], strides: ints = [3, 2]> ("p2o.pd_op.hardswish.27.0")
   val_42 = Mul (val_41, "learnable_affine_block_55.w_0")
   "p2o.pd_op.pool2d.2.0" = Add (val_42, "learnable_affine_block_55.w_1")
   "p2o.pd_op.batch_norm_.1.0" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 3], pads: ints = [0, 1, 0, 1], strides: ints = [1, 1]> ("p2o.pd_op.pool2d.2.0", "conv2d_131.w_0", "conv2d_131.w_0_bias")
   ["Sigmoid.0"] "Sigmoid.1" = Sigmoid ("p2o.pd_op.batch_norm_.1.0")
   ["Mul.116"] "p2o.pd_op.swish.0.0" = Mul ("p2o.pd_op.batch_norm_.1.0", "Sigmoid.1")
   "p2o.pd_op.batch_norm_.2.0" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.swish.0.0", "conv2d_132.w_0", "conv2d_132.w_0_bias")
   ["Sigmoid.2"] "Sigmoid.3" = Sigmoid ("p2o.pd_op.batch_norm_.2.0")
   ["Mul.117"] "p2o.pd_op.swish.1.0" = Mul ("p2o.pd_op.batch_norm_.2.0", "Sigmoid.3")
   "p2o.pd_op.flatten.0.0" = Squeeze ("p2o.pd_op.swish.1.0", "helper.constant.9")
   ["Transpose.0"] "p2o.pd_op.transpose.0.0" = Transpose <perm: ints = [0, 2, 1]> ("p2o.pd_op.flatten.0.0")
   "Add.181" = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> ("p2o.pd_op.transpose.0.0", "helper.reshape.0", "helper.reshape.1")
   ["MatMul.0"] "p2o.pd_op.matmul.0.0" = MatMul ("Add.181", "linear_0.w_0_qkv_scaled")
   ["Add.182"] "Add.183" = Add ("p2o.pd_op.matmul.0.0", "linear_0.b_0_qkv_scaled")
   "Add.183_qkv" = Reshape <allowzero: int = 0> ("Add.183", "Add.183_qkv_shape")
   "Add.183_qkv_heads" = Transpose <perm: ints = [0, 2, 1, 3]> ("Add.183_qkv")
   "Add.183_q", "Add.183_k", "Add.183_v" = Split <axis: int = 1> ("Add.183_qkv_heads", "Add.183_qkv_sizes")
   ["Transpose.2"] "p2o.pd_op.transpose.2.0" = Transpose <perm: ints = [0, 1, 3, 2]> ("Add.183_k")
   ["MatMul.1"] "p2o.pd_op.matmul.1.0" = MatMul ("Add.183_q", "p2o.pd_op.transpose.2.0")
   ["Softmax.0"] "p2o.pd_op.softmax.0.0" = Softmax <axis: int = -1> ("p2o.pd_op.matmul.1.0")
   ["MatMul.2"] "p2o.pd_op.matmul.2.0" = MatMul ("p2o.pd_op.softmax.0.0", "Add.183_v")
   ["Transpose.3"] "p2o.pd_op.transpose.3.0" = Transpose <perm: ints = [0, 2, 1, 3]> ("p2o.pd_op.matmul.2.0")
   ["Reshape.38"] "p2o.pd_op.reshape.33.0" = Reshape <allowzero: int = 0> ("p2o.pd_op.transpose.3.0", "auto.cast.38")
   ["MatMul.3"] "p2o.pd_op.matmul.3.0" = MatMul ("p2o.pd_op.reshape.33.0", "linear_1.w_0")
   ["Add.184"] "Add.185" = Add ("p2o.pd_op.matmul.3.0", "linear_1.b_0")
   ["Add.186"] "Add.187" = Add ("p2o.pd_op.transpose.0.0", "Add.185")
   "Add.191" = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> ("Add.187", "helper.reshape.2", "helper.reshape.3")
   ["MatMul.4"] "p2o.pd_op.matmul.4.0" = MatMul ("Add.191", "linear_2.w_0")
   ["Add.192"] "Add.193" = Add ("p2o.pd_op.matmul.4.0", "linear_2.b_0")
   ["Sigmoid.4"] "Sigmoid.5" = Sigmoid ("Add.193")
   ["Mul.124"] "p2o.pd_op.swish.2.0" = Mul ("Add.193", "Sigmoid.5")
   ["MatMul.5"] "p2o.pd_op.matmul.5.0" = MatMul ("p2o.pd_op.swish.2.0", "linear_3.w_0")
   ["Add.194"] "Add.195" = Add ("p2o.pd_op.matmul.5.0", "linear_3.b_0")
   ["Add.196"] "Add.197" = Add ("Add.187", "Add.195")
   "Add.201" = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> ("Add.197", "helper.reshape.4", "helper.reshape.5")
   ["MatMul.6"] "p2o.pd_op.matmul.6.0" = MatMul ("Add.201", "linear_4.w_0_qkv_scaled")
   ["Add.202"] "Add.203" = Add ("p2o.pd_op.matmul.6.0", "linear_4.b_0_qkv_scaled")
   "Add.203_qkv" = Reshape <allowzero: int = 0> ("Add.203", "Add.183_qkv_shape")
   "Add.203_qkv_heads" = Transpose <perm: ints = [0, 2, 1, 3]> ("Add.203_qkv")
   "Add.203_q", "Add.203_k", "Add.203_v" = Split <axis: int = 1> ("Add.203_qkv_heads", "Add.183_qkv_sizes")
   ["Transpose.5"] "p2o.pd_op.transpose.5.0" = Transpose <perm: ints = [0, 1, 3, 2]> ("Add.203_k")
   ["MatMul.7"] "p2o.pd_op.matmul.7.0" = MatMul ("Add.203_q", "p2o.pd_op.transpose.5.0")
   ["Softmax.1"] "p2o.pd_op.softmax.1.0" = Softmax <axis: int = -1> ("p2o.pd_op.matmul.7.0")
   ["MatMul.8"] "p2o.pd_op.matmul.8.0" = MatMul ("p2o.pd_op.softmax.1.0", "Add.203_v")
   ["Transpose.6"] "p2o.pd_op.transpose.6.0" = Transpose <perm: ints = [0, 2, 1, 3]> ("p2o.pd_op.matmul.8.0")
   ["Reshape.46"] "p2o.pd_op.reshape.35.0" = Reshape <allowzero: int = 0> ("p2o.pd_op.transpose.6.0", "auto.cast.38")
   ["MatMul.9"] "p2o.pd_op.matmul.9.0" = MatMul ("p2o.pd_op.reshape.35.0", "linear_5.w_0")
   ["Add.204"] "Add.205" = Add ("p2o.pd_op.matmul.9.0", "linear_5.b_0")
   ["Add.206"] "Add.207" = Add ("Add.197", "Add.205")
   "Add.211" = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> ("Add.207", "helper.reshape.6", "helper.reshape.7")
   ["MatMul.10"] "p2o.pd_op.matmul.10.0" = MatMul ("Add.211", "linear_6.w_0")
   ["Add.212"] "Add.213" = Add ("p2o.pd_op.matmul.10.0", "linear_6.b_0")
   ["Sigmoid.6"] "Sigmoid.7" = Sigmoid ("Add.213")
   ["Mul.131"] "p2o.pd_op.swish.3.0" = Mul ("Add.213", "Sigmoid.7")
   ["MatMul.11"] "p2o.pd_op.matmul.11.0" = MatMul ("p2o.pd_op.swish.3.0", "linear_7.w_0")
   ["Add.214"] "Add.215" = Add ("p2o.pd_op.matmul.11.0", "linear_7.b_0")
   ["Add.216"] "Add.217" = Add ("Add.207", "Add.215")
   "Add.221" = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> ("Add.217", "helper.reshape.8", "helper.reshape.9")
   "p2o.pd_op.reshape.36.0" = Unsqueeze ("Add.221", rec_unsqueeze_axis)
   ["Transpose.7"] "p2o.pd_op.transpose.7.0" = Transpose <perm: ints = [0, 3, 1, 2]> ("p2o.pd_op.reshape.36.0")
   "p2o.pd_op.batch_norm_.3.0" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.transpose.7.0", "conv2d_133.w_0", "conv2d_133.w_0_bias")
   ["Sigmoid.8"] "Sigmoid.9" = Sigmoid ("p2o.pd_op.batch_norm_.3.0")
   ["Mul.134"] "p2o.pd_op.swish.4.0" = Mul ("p2o.pd_op.batch_norm_.3.0", "Sigmoid.9")
   ["Concat.3"] "Concat.4" = Concat <axis: int = 1> ("p2o.pd_op.pool2d.2.0", "p2o.pd_op.swish.4.0")
   "p2o.pd_op.batch_norm_.4.0" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 3], pads: ints = [0, 1, 0, 1], strides: ints = [1, 1]> ("Concat.4", "conv2d_134.w_0", "conv2d_134.w_0_bias")
   ["Sigmoid.10"] "Sigmoid.11" = Sigmoid ("p2o.pd_op.batch_norm_.4.0")
   ["Mul.135"] "p2o.pd_op.swish.5.0" = Mul ("p2o.pd_op.batch_norm_.4.0", "Sigmoid.11")
   "p2o.pd_op.batch_norm_.5.0" = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("p2o.pd_op.swish.5.0", "conv2d_135.w_0", "conv2d_135.w_0_bias")
   ["Sigmoid.12"] "Sigmoid.13" = Sigmoid ("p2o.pd_op.batch_norm_.5.0")
   ["Mul.136"] "p2o.pd_op.swish.6.0" = Mul ("p2o.pd_op.batch_norm_.5.0", "Sigmoid.13")
   ["Squeeze.8"] "p2o.pd_op.squeeze.0.0" = Squeeze ("p2o.pd_op.swish.6.0", "helper.constant.9")
   ["Transpose.8"] "p2o.pd_op.transpose.8.0" = Transpose <perm: ints = [0, 2, 1]> ("p2o.pd_op.squeeze.0.0")
   ["MatMul.12"] "p2o.pd_op.matmul.12.0" = MatMul ("p2o.pd_op.transpose.8.0", "linear_8.w_0")
   ["Add.222"] "Add.223" = Add ("p2o.pd_op.matmul.12.0", "linear_8.b_0")
   [n0_2] tmp_2 = ArgMax <axis: int = 2, keepdims: int = 0> ("Add.223")
   [n1_2] ctc_indices = Cast <to: int = 6> (tmp_2)
   [n3_2] max_logits = ReduceMax <keepdims: int = 1> ("Add.223", "helper.constant.9")
   [n4_2] tmp_0_2 = Sub ("Add.223", max_logits)
   [n5] tmp_1 = Exp (tmp_0_2)
   [n7] tmp_3 = ReduceSum <keepdims: int = 0> (tmp_1, "helper.constant.9")
   [n8] ctc_confidence = Reciprocal (tmp_3)
}

weights:
Add.183_qkv_shape INT64[4] 003762987fc4
Add.183_qkv_sizes INT64[3] 49f92c4a88e0
auto.cast.38 INT64[3] b190f2a09873
const_cast FLOAT[] 9fd754fbfd83
conv2d_0.w_0 FLOAT[16,3,3,3] 34df4281f729
conv2d_0.w_0_bias FLOAT[16] 105e1f48c3a4
conv2d_107.w_0 FLOAT[120,480,1,1] d2d0e602dcd4
conv2d_108.w_0 FLOAT[480,120,1,1] 54272c2fc604
conv2d_131.w_0 FLOAT[60,480,1,3] 9cc62aeb7e32
conv2d_131.w_0_bias FLOAT[60] 463c0e2a9bdc
conv2d_132.w_0 FLOAT[120,60,1,1] 6273465d3fca
conv2d_132.w_0_bias FLOAT[120] 80df00d04058
conv2d_133.w_0 FLOAT[480,120,1,1] 387d4d6defab
conv2d_133.w_0_bias FLOAT[480] 8dec489e72b9
conv2d_134.w_0 FLOAT[60,960,1,3] 3a6a1b6b26d2
conv2d_134.w_0_bias FLOAT[60] 241ae9d4fba9
conv2d_135.w_0 FLOAT[120,60,1,1] 9247d4a073a4
conv2d_135.w_0_bias FLOAT[120] 9e33a4861e05
conv2d_136.w_0_lab FLOAT[16,1,3,3] 882eed7d616a
conv2d_137.w_0_lab_lab FLOAT[32,16,1,1] 91240265a23a
conv2d_138.w_0_lab_laboffset FLOAT[] 3af84f5cb37c
conv2d_138.w_0_lab_labscale FLOAT[32,1,3,3] 6f3f14baf376
conv2d_139.w_0_lab_lab FLOAT[64,32,1,1] 360bc077c422
conv2d_140.w_0_lab_laboffset FLOAT[] ba3f0a44cb9d
conv2d_140.w_0_lab_labscale FLOAT[64,1,3,3] d98969f8dc48
conv2d_141.w_0_lab_lab FLOAT[64,64,1,1] 22d3912e3b2c
conv2d_142.w_0_lab_laboffset FLOAT[] 34eb1d1cb273
conv2d_142.w_0_lab_labscale FLOAT[64,1,3,3] de45e41b33ac
conv2d_143.w_0_lab_lab FLOAT[128,64,1,1] 97476cba5139
conv2d_144.w_0_lab_laboffset FLOAT[] 5b7017a87ec6
conv2d_144.w_0_lab_labscale FLOAT[128,1,3,3] 571e48d95cbe
conv2d_145.w_0_lab_lab FLOAT[128,128,1,1] 23aa9c5596ae
conv2d_146.w_0_lab_laboffset FLOAT[] 52b7962899a9
conv2d_146.w_0_lab_labscale FLOAT[128,1,3,3] e4ca13a8ddd5
conv2d_147.w_0_lab_lab FLOAT[240,128,1,1] 3585c9631761
conv2d_148.w_0_lab_laboffset FLOAT[] 3ac71f1a131b
conv2d_148.w_0_lab_labscale FLOAT[240,1,5,5] 59f3c8906d08
conv2d_149.w_0_lab_lab FLOAT[240,240,1,1] 70deb1cafa9b
conv2d_150.w_0_lab_laboffset FLOAT[] d1690af3307f
conv2d_150.w_0_lab_labscale FLOAT[240,1,5,5] 0a0faefb0475
conv2d_151.w_0_lab_lab FLOAT[240,240,1,1] 754032ccee3b
conv2d_152.w_0_lab_laboffset FLOAT[] 2209f068c6fe
conv2d_152.w_0_lab_labscale FLOAT[240,1,5,5] 254c5dbcf414
conv2d_153.w_0_lab_lab FLOAT[240,240,1,1] 1d33db0c2dd4
conv2d_154.w_0_lab_laboffset FLOAT[] c2b6b5366d2f
conv2d_154.w_0_lab_labscale FLOAT[240,1,5,5] 43d670509d34
conv2d_155.w_0_lab_lab FLOAT[240,240,1,1] 5d893afc4586
conv2d_156.w_0_lab_laboffset FLOAT[] 3f5ff85e6d1b
conv2d_156.w_0_lab_labscale FLOAT[240,1,5,5] a73bca002144
conv2d_157.w_0_lab FLOAT[480,240,1,1] 65f2f72963a0
conv2d_158.w_0_lab_laboffset FLOAT[] c01ea59574f3
conv2d_158.w_0_lab_labscale FLOAT[480,1,5,5] 57abbc03e61a
conv2d_159.w_0_lab FLOAT[480,480,1,1] c995d038d333
conv2d_160.w_0_lab_laboffset FLOAT[] 05a6b61cdacd
conv2d_160.w_0_lab_labscale FLOAT[480,1,5,5] 843f31741161
conv2d_161.w_0_lab_lab FLOAT[480,480,1,1] 30258baac013
conv2d_162.w_0_lab_laboffset FLOAT[] c75eaa0e917f
conv2d_162.w_0_lab_labscale FLOAT[480,1,5,5] 558cd9018869
conv2d_163.w_0_lab_lab FLOAT[480,480,1,1] f87831201cc0
conv2d_96.w_0 FLOAT[60,240,1,1] ab552f547bfd
conv2d_97.w_0 FLOAT[240,60,1,1] 219d3804e0f9
helper.constant.9 INT64[1] d86e8112f3c4
helper.reshape.0 FLOAT[120] a95eba1df2c1
helper.reshape.1 FLOAT[120] dd4096eae7bb
helper.reshape.2 FLOAT[120] 02a8a48a3ad9
helper.reshape.3 FLOAT[120] 0567c9663a13
helper.reshape.4 FLOAT[120] 88b348a46bae
helper.reshape.5 FLOAT[120] ba47abcac5c2
helper.reshape.6 FLOAT[120] aed9ffb9191d
helper.reshape.7 FLOAT[120] 4a9fbf95eb59
helper.reshape.8 FLOAT[120] 49d0bb05fe10
helper.reshape.9 FLOAT[120] e8034e7a2162
learnable_affine_block_41.w_0 FLOAT[1] a76ac811e4e2
learnable_affine_block_41.w_1 FLOAT[1] f4475e39b3a2
learnable_affine_block_45.w_0 FLOAT[1] 1d81fef0f259
learnable_affine_block_45.w_1 FLOAT[1] da21e4ca8ce4
learnable_affine_block_55.w_0 FLOAT[1] 0df6ea541c62
learnable_affine_block_55.w_1 FLOAT[1] 5473b1fddf91
linear_0.b_0_qkv_scaled FLOAT[360] 7ceb4d414364
linear_0.w_0_qkv_scaled FLOAT[120,360] 4aa391da940d
linear_1.b_0 FLOAT[120] 3720201dc965
linear_1.w_0 FLOAT[120,120] 3c4d20984273
linear_2.b_0 FLOAT[240] bbc55bb7b4ea
linear_2.w_0 FLOAT[120,240] 25faa7183517
linear_3.b_0 FLOAT[120] dc336862d5ae
linear_3.w_0 FLOAT[240,120] ed26f23747f2
linear_4.b_0_qkv_scaled FLOAT[360] ba4b2a7fedf3
linear_4.w_0_qkv_scaled FLOAT[120,360] 4f9667f45038
linear_5.b_0 FLOAT[120] 8e1c4ab966c8
linear_5.w_0 FLOAT[120,120] 9eff89d7ce09
linear_6.b_0 FLOAT[240] 0d6a49832bc1
linear_6.w_0 FLOAT[120,240] e7fd1cdcbf33
linear_7.b_0 FLOAT[120] f9b16daaa082
linear_7.w_0 FLOAT[240,120] 4ae86a74e1c6
linear_8.b_0 FLOAT[438] c6737f64d40d
linear_8.w_0 FLOAT[120,438] 03285bb134ec
p2o.pd_op.conv2d.1.0_bias_lab_lab FLOAT[32] 7aa5d9fee267
p2o.pd_op.conv2d.10.0_bias_lab_lab FLOAT[240] 3502847e7fb1
p2o.pd_op.conv2d.11.0_bias FLOAT[60] d99fd1b549fb
p2o.pd_op.conv2d.12.0_bias FLOAT[240] b221bbc745aa
p2o.pd_op.conv2d.13.0_bias_lab FLOAT[480] db53118362d3
p2o.pd_op.conv2d.14.0_bias FLOAT[120] d24bad2a28a0
p2o.pd_op.conv2d.15.0_bias FLOAT[480] 67ea0d2e8d77
p2o.pd_op.conv2d.16.0_bias_lab FLOAT[480] 2b5ef9f63456
p2o.pd_op.conv2d.17.0_bias_lab_lab FLOAT[480] c584b9daef76
p2o.pd_op.conv2d.18.0_bias_lab_lab FLOAT[480] 12c64e71133e
p2o.pd_op.conv2d.2.0_bias_lab_lab FLOAT[64] 091974adb85f
p2o.pd_op.conv2d.3.0_bias_lab_lab FLOAT[64] 408e43f10893
p2o.pd_op.conv2d.4.0_bias_lab_lab FLOAT[128] f3f71737ace8
p2o.pd_op.conv2d.5.0_bias_lab_lab FLOAT[128] d3f630e2cc43
p2o.pd_op.conv2d.6.0_bias_lab_lab FLOAT[240] 6cc72581a18e
p2o.pd_op.conv2d.7.0_bias_lab_lab FLOAT[240] 7f76813a0764
p2o.pd_op.conv2d.8.0_bias_lab_lab FLOAT[240] 6b037b067778
p2o.pd_op.conv2d.9.0_bias_lab_lab FLOAT[240] 733286ec6907
p2o.pd_op.depthwise_conv2d.0.0_bias_lab FLOAT[16] d2d86949eede
p2o.pd_op.depthwise_conv2d.1.0_bias_lab FLOAT[32] cb5966d61b11
p2o.pd_op.depthwise_conv2d.10.0_bias_lab FLOAT[240] f4dfbd05f740
p2o.pd_op.depthwise_conv2d.11.0_bias_lab FLOAT[480] 0c9931a92f7a
p2o.pd_op.depthwise_conv2d.12.0_bias_lab FLOAT[480] 3c17c69734b2
p2o.pd_op.depthwise_conv2d.13.0_bias_lab FLOAT[480] 6e0ade206f4d
p2o.pd_op.depthwise_conv2d.2.0_bias_lab FLOAT[64] 1e031ef81848
p2o.pd_op.depthwise_conv2d.3.0_bias_lab FLOAT[64] b379432f1ab6
p2o.pd_op.depthwise_conv2d.4.0_bias_lab FLOAT[128] b7527030fb10
p2o.pd_op.depthwise_conv2d.5.0_bias_lab FLOAT[128] 0b74e3d7dea7
p2o.pd_op.depthwise_conv2d.6.0_bias_lab FLOAT[240] 4f3a618f3948
p2o.pd_op.depthwise_conv2d.7.0_bias_lab FLOAT[240] c2203c76fb14
p2o.pd_op.depthwise_conv2d.8.0_bias_lab FLOAT[240] e87a59914a91
p2o.pd_op.depthwise_conv2d.9.0_bias_lab FLOAT[240] f9bc97532929
rec_unsqueeze_axis INT64[1] 7c9fa136d441
