问题

以下是代码部分的翻译：

让我们假设在我的函数中，我需要处理形状为[4,3,2,1]和[5,4,3,2,1]的输入张量。我想以这样的方式重塑它们，使最后两个维度交换，例如，变成[4,3,1,2]。在即时执行模式下，这很容易，但当我尝试使用`@tf.function`包装我的函数时，会出现以下错误：

`OperatorNotAllowedInGraphError: 不允许在符号化的tf.Tensor上进行迭代：AutoGraph确实转换了此函数。这可能表明您正在尝试使用不受支持的功能。`

相关代码如下：

```python
tensor = tf.random.uniform(shape=[4, 3, 2, 1])

@tf.function
def my_func():
    reshaped = tf.reshape(tensor, shape=[*tf.shape(tensor)[:-2], tf.shape(tensor)[-1], tf.shape(tensor)[-2]])
    return reshaped

logging.info(my_func())

看起来 TensorFlow 不喜欢[:-2]的表示方式，但我真的不知道我应该如何以一种优雅和易读的方式解决这个问题。

<details>
<summary>英文:</summary>

Let&#39;s say that in my function I have to deal with input tensors of shape [4,3,2,1] and [5,4,3,2,1]. I want to reshape them in such a way that the last two dimensions are swapped, e.g., to [4,3,1,2]. In eager mode this is easy, but when I try to wrap my function using `@tf.function` the following error is thrown:

`OperatorNotAllowedInGraphError: Iterating over a symbolic tf.Tensor is not allowed: AutoGraph did convert this function. This might indicate you are trying to use an unsupported feature.`

The code in question is as follows:

    tensor = tf.random.uniform(shape=[4, 3, 2, 1])
    
        @tf.function
        def my_func():
            reshaped = tf.reshape(tensor, shape=[*tf.shape(tensor)[:-2], tf.shape(tensor)[-1], tf.shape(tensor)[-2]])
            return reshaped
    
        logging.info(my_func())

It looks like tensorflow does not like the `[:-2]` notation, but I don&#39;t really know how else I should solve this problem in an elegant and well-readable way.



</details>


# 答案1
**得分**: 1

在“图”模式下切片张量的方式不起作用，但我认为您可以使用`tf.transpose`：
```python
import tensorflow as tf

tensor = tf.random.uniform(shape=[4, 3, 2, 1])

@tf.function
def my_func():
  rank = tf.rank(tensor)
  some_magic = tf.concat([tf.zeros((rank - 2,), dtype=tf.int32), [1, -1]], axis=-1)
  reshaped = tf.transpose(tensor, perm = tf.range(rank) + some_magic)
  return reshaped

print(my_func().shape)
# (4, 3, 1, 2)

import tensorflow as tf

tensor = tf.random.uniform(shape=[4, 3, 2, 1])

@tf.function
def my_func():
  rank = tf.rank(tensor)
  some_magic = tf.concat([tf.zeros((rank - 2,), dtype=tf.int32), [1, -1]], axis=-1)
  reshaped = tf.transpose(tensor, perm = tf.range(rank) + some_magic)
  return reshaped

print(my_func().shape)
# (4, 3, 1, 2)