text: import copy
  
  import tensorflow as tf
  
  from onnx_tf.handlers.backend_handler import BackendHandler
  from onnx_tf.handlers.handler import onnx_op
  from onnx_tf.handlers.handler import tf_func
  from onnx_tf.common.tf_helper import tf_shape
  
  
  @onnx_op("Split")
  @tf_func(tf.split)
  class Split(BackendHandler):
  
    @classmethod
    def args_check(cls, node, **kwargs):
      axis = node.attrs.get("axis", 0)
      x_rank =  len(kwargs["tensor_dict"][node.inputs[0]].get_shape().as_list())
      if axis > x_rank - 1 or axis < -x_rank:
          raise ValueError("Axis is out of bound")
  
    @classmethod
    def get_attrs_processor_param(cls):
      return {"default": {"axis": 0}}
  
    @classmethod
    def _common(cls, node, **kwargs):
      tensor_dict = kwargs["tensor_dict"]
      input = tensor_dict[node.inputs[0]]
      x_shape = tf_shape(input)
      attrs = copy.deepcopy(node.attrs)
      axis = attrs.get("axis", 0)
      axis = axis if axis >= 0 else len(x_shape) + axis
      if "split" in node.attrs:
        split = attrs["split"]
      elif len(node.inputs) == 2:  # since version 1
        split = tensor_dict[node.inputs[1]]
      else:
        per_part = x_shape[axis] / len(node.outputs)
        if input.shape.is_fully_defined():
          if int(per_part) != per_part:
            raise ValueError("Split can not be evenly divided.")
          split = [int(per_part)] * len(node.outputs)
        else:
          split = [tf.cast(per_part, tf.int32)] * len(node.outputs)
      attrs["num_or_size_splits"] = split
      return list(
          cls.make_tensor_from_onnx_node(
              node, inputs=[input], attrs=attrs, **kwargs))
  
    @classmethod
    def version_1(cls, node, **kwargs):
      return cls._common(node, **kwargs)
  
    @classmethod
    def version_2(cls, node, **kwargs):
      return cls._common(node, **kwargs)
  
    @classmethod
    def version_11(cls, node, **kwargs):
      return cls._common(node, **kwargs)
  