text: 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 partial_support
  from onnx_tf.handlers.handler import ps_description
  
  @onnx_op("ConcatFromSequence")
  @partial_support(True)
  @ps_description("new_axis=1 not supported in Tensorflow.")
  class ConcatFromSequence(BackendHandler):
  
    @classmethod
    def version_11(cls, node, **kwargs):
      # get the sequence and convert to a tensor
      tensor_dict = kwargs["tensor_dict"]
      input_sequence = tensor_dict[node.inputs[0]]
      output_tensor = tf.sparse.to_dense(input_sequence.to_sparse())
      i_min = 0
      i_max = tf.shape(output_tensor)[0]
  
      axis = node.attrs.get("axis")
      new_axis = node.attrs.get("new_axis", 0)
  
      if new_axis == 1:
        # Currently this case, np.stack like behavior, is not supported.
        # The commmented code below would work if tf.unstack supports
        # a scalar tensor input for num.
        # tensor_list = tf.unstack(output_tensor, num=i_max)
        # output_tensor = tf.stack(tensor_list, axis)
        raise RuntimeError(
            "Concat from sequence with new_axis=1 not supported in Tensorflow.")
      else:
        # define the condition and body for the while loop
        cond_less = lambda i1, i2, i3, axis, o1: tf.less(i1, i2)
        body_concat = lambda i1, i2, i3, axis, o1: [
            i1 + 1, i2, i3, axis,
            tf.concat([o1, tf.gather(i3, i1)], axis=axis)
        ]
  
        # initialize with the first element
        t = tf.gather(output_tensor, 0)
  
        # setup inputs for the while loop
        input_tensor = tf.gather(output_tensor, tf.range(1, i_max))
        i_max = i_max - 1
  
        # loop through the rest of elements
        _, _, _, _, output_tensor = tf.while_loop(
            cond_less,
            body_concat, [i_min, i_max, input_tensor, axis, t],
            shape_invariants=[
                tf.TensorShape(None),
                i_max.get_shape(),
                input_tensor.get_shape(),
                tf.TensorShape(None),
                tf.TensorShape(None)
            ],
            parallel_iterations=1)
  
      return [output_tensor]
  