code: def convert_SoftmaxCrossEntropy(
      func, opset_version, input_names, output_names, context
  ):
      # obtain input variable
      if not isinstance(func, chainer.FunctionNode):
          raise NotImplementedError(
              "SoftmaxCrossEntropy is currently supported for Chainer>=6.0.0a1."
          )
  
      x_var, t_var = func.get_retained_inputs()
      if len(x_var.shape) != 2:
          raise NotImplementedError(
              "ONNX-Chainer currently handles SoftmaxCrossEntropy only when "
              "the dimension of input variable x is exactly two."
          )
      if np.any(t_var.array == func.ignore_label):
          raise NotImplementedError(
              "ONNX-Chainer currently handles SoftmaxCrossEntropy only when "
              "ignore_label is not used in input variable t."
          )
      if (
          (not func.normalize)
          or (func.class_weight is not None)
          or (func.ignore_label != -1)
          or (func.reduce != "mean")
      ):
          raise NotImplementedError(
              "ONNX-Chainer currently handles SoftmaxCrossEntropy only when "
              "argument parameters are default setting."
          )
  
      # create intermediate values
      gb = onnx_helper.GraphBuilder()
      x, t = input_names
      y_log = gb.op("LogSoftmax", [x])
      depth = context.add_const(
          np.array([x_var.shape[1]], dtype=np.int32), "depth"
      )
      zeroone = context.add_const(
          np.array([0, 1], dtype=x_var.dtype), "zeroone"
      )
      th = gb.op("OneHot", [t, depth, zeroone])
      s0 = gb.op("Mul", [y_log, th])
      sn = gb.op("Neg", [s0])
      sr = gb.op("ReduceSum", [sn], axes=[1], keepdims=0)
      gb.op_output_named(
          "ReduceMean", [sr], output_names, axes=[0], keepdims=0
      )
  
      return gb.nodes()
  
imports: import chainer
  import numpy
  from onnx_chainer.functions.opset_version import support
  from onnx_chainer import onnx_helper
label_desc: Use image node_id 1 for calling a global function with example usage: convert_SoftmaxCrossEntropy(func, opset_version, input_names, output_names, context) and returns: gb