code: def convert_TransposeSequence(
      func, opset_version, input_names, output_names, context
  ):
      if any(x.shape != func.inputs[0].shape for x in func.inputs):
          raise ValueError(
              "ONNX-Chainer can convert TransposeSequence only when all "
              "inputs have same shape"
          )
      gb = onnx_helper.GraphBuilder()
      n = func.inputs[0].shape[0]
  
      concat_out = gb.op(
          "Concat",
          [
              gb.op("Unsqueeze", [name], axes=[0])
              for name in input_names
          ],
          axis=0,
      )
  
      perm = list(range(len(func.inputs[0].shape) + 1))
      perm[0], perm[1] = perm[1], perm[0]
      transpose_out = gb.op("Transpose", [concat_out], perm=perm)
  
      split_outs = gb.op(
          "Split", [transpose_out], axis=0, num_outputs=n
      )
      if n == 1:
          split_outs = [split_outs]
      for i, name in enumerate(split_outs):
          gb.op_output_named(
              "Squeeze", [name], [output_names[i]], axes=[0]
          )
  
      return gb.nodes()
  
imports: import warnings
  import chainer
  import numpy
  import onnx
  from onnx.mapping import NP_TYPE_TO_TENSOR_TYPE
  from onnx_chainer.functions.opset_version import support
  from onnx_chainer import onnx_helper
label_desc: Use image node_id 30 for calling a global function with example usage: convert_TransposeSequence(func, opset_version, input_names, output_names, context) and returns: gb