def _transpose(converter, node: Any, inputs: List[str]) -> Any:
    x_in = converter.outputs[inputs[0]]
    perm = converter.outputs[inputs[1]]
    tensor = perm.attr['value'].tensor
    shape = [i.size for i in tensor.tensor_shape.dim]
    dtype = perm.attr['dtype'].type
    if (dtype == tf.int32):
        nums = array.array('i', tensor.tensor_content)
    elif (dtype == tf.int64):
        nums = array.array('l', tensor.tensor_content)
    else:
        raise TypeError('Unsupported dtype for transpose perm')
    return converter.protocol.transpose(x_in, np.array(nums).reshape(shape))
def _transpose(converter, node: Any, inputs: List[str]) -> Any:
    x_in = converter.outputs[inputs[0]]
    perm = converter.outputs[inputs[1]]
    tensor = perm.attr['value'].tensor
    shape = [i.size for i in tensor.tensor_shape.dim]
    dtype = perm.attr['dtype'].type
    if (dtype == tf.int32):
        nums = array.array('i', tensor.tensor_content)
    elif (dtype == tf.int64):
        nums = array.array('l', tensor.tensor_content)
    else:
        raise TypeError('Unsupported dtype for transpose perm')
    return converter.protocol.transpose(x_in, np.array(nums).reshape(shape))