}
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/biases/read"
  op: "Identity"
  input: "BoxPredictor_4/ClassPredictor/biases"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@BoxPredictor_4/ClassPredictor/biases"
      }
    }
  }
}
node {

 private:
  // Whether the value is used as a bias input of another op. Here we assume
  // bias is used immediately by the user. This assumption is always correct
  // after constant folding.
  bool UsedAsBias(Value value) {
    for (auto &use : value.getUses()) {
      auto biases = TFL::GetOpQuantSpec(use.getOwner())->biases_params;
      if (biases.find(use.getOperandNumber()) != biases.end()) return true;
    }
    return false;
  }

      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/biases/read"
  op: "Identity"
  input: "BoxPredictor_4/ClassPredictor/biases"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@BoxPredictor_4/ClassPredictor/biases"
      }
    }
  }
}
node {

      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/biases/read"
  op: "Identity"
  input: "BoxPredictor_4/ClassPredictor/biases"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@BoxPredictor_4/ClassPredictor/biases"
      }
    }
  }
}
node {

      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/biases/read"
  op: "Identity"
  input: "BoxPredictor_4/ClassPredictor/biases"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@BoxPredictor_4/ClassPredictor/biases"
      }
    }
  }
}
node {

          GetQuantScaleSpec(user);
      const BiasParamsMap biases = spec->biases_params;

      // The quantization parameters of a `weight` shouldn't be determined by
      // other values. So any constants which are not bias, an operand of an
      // op with same scale requirements, and haven't been quantized are
      // weights.
      if (!biases.contains(operand_num) &&
          !scale_spec->has_same_scale_requirement &&

        return;

      // We only fold int32/int64 for Const and i32 for QConst if not specify
      // all constants flag. (Since they're more like "configs" or i32 biases.)
      // We will fold every const ops (and q_const ops) if we speicfy the
      // fold_all_constants_flag.
      if (!fold_all_constants_flag_) {
        if (!IsConstOrQConstInt(op)) return;
      }

struct OpQuantSpec {
  // Maps the operand index of a bias input to its quantization specifications,
  // including the non-bias operand indexes and the method retrieving
  // quantization parameters from list of parameters of the non-bias operands.
  // This map is empty if the op doesn't have a bias operand.
  BiasParamsMap biases_params;

  %bias = arith.constant dense<1.0> : tensor<32xf32>
  %input = "tfl.dequantize"(%arg0) : (tensor<1x224x224x3x!quant.uniform<u8:f32, 1.5>>) -> tensor<1x224x224x3xf32>
  %weight = "tfl.dequantize"(%arg1) : (tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32:3, {1.0,2.0,3.0}>>) -> tensor<32x3x3x3xf32>
  %conv = "tfl.conv_2d"(%input, %weight, %bias) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32,

      // TODO(b/172517537): make this work with non-PTQ case.
      if (llvm::isa<func::ConstantOp, arith::ConstantOp, TFL::ConstOp>(
              input.getDefiningOp())) {
        // Tensors with derived scale are biases, and handled in propagation.
        if (tensor_property.use_derived_scale) continue;
        // For weights, use quantization scale inferred from the values.
        if (failed(processConstantOp(op, input.getDefiningOp(), index,