def smart_reshape(tensor: tf.Tensor, shape: List[Optional[Union[int, tf.Tensor]]]) -> tf.Tensor:
    """A version of tf.reshape.
    1. The ouput tensor is always of the same rank as input tensor.
    2. The parameter shape is supposed to be a list that is smaller or equal
    than the tensor shape.
    3. The list shape may contain None, that means "keep this dimension unchanged".
    4. The list shape is appended with None value to be of the same length as the input tensor shape.
    5. Like for tf.reshape output tensor does not requre new memory for allocation.

    For example:
    ```python
        smart_reshape(
            tensor=tf.zeros(shape=[2, 3, 4, 5]),
            shape=[8, None, 1]
        )
        # -> tf.Tensor([8, 3, 1, 5])
    ```

    Args:
        tensor: tf.Tensor of any shape and type
        shape:  list of optional static of dynamic integrates

    Returns:    tf.Tensor of the same typey and rank as the input tensor
    """
    if len(shape) > len(tensor.shape):
        raise ValueError(f"Tensor with shape {tensor.shape} cannot be reshaped to {shape}.")

    shape = shape + [None] * (len(tensor.shape) - len(shape))

    original_shape = tf.shape(tensor)
    new_shape = []
    for index, dim in enumerate(shape):
        if dim is None:
            new_shape.append(original_shape[index])
        else:
            new_shape.append(dim)

    return tf.reshape(tensor=tensor, shape=new_shape)
A version of tf.reshape.
    1. The ouput tensor is always of the same rank as input tensor.
    2. The parameter shape is supposed to be a list that is smaller or equal
    than the tensor shape.
    3. The list shape may contain None, that means "keep this dimension unchanged".
    4. The list shape is appended with None value to be of the same length as the input tensor shape.
    5. Like for tf.reshape output tensor does not requre new memory for allocation.

    For example:
    ```python
        smart_reshape(
            tensor=tf.zeros(shape=[2, 3, 4, 5]),
            shape=[8, None, 1]
        )
        # -> tf.Tensor([8, 3, 1, 5])
    ```

    Args:
        tensor: tf.Tensor of any shape and type
        shape:  list of optional static of dynamic integrates

    Returns:    tf.Tensor of the same typey and rank as the input tensor
    
tensorflow_asr/losses/impl/ctc_tpu.py