Repository: tensorflow
Issue: Unspecific error message "None values not supported" when no labels are provided in dataset
Labels: type:feature,comp:data,type:docs-feature

### Issue type

Documentation Feature Request

### Have you reproduced the bug with TensorFlow Nightly?

No

### Source

source

### TensorFlow version

2.18.0

### Custom code

Yes

### OS platform and distribution

_No response_

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

If I write some tf/keras code that fails to include a label in the dataset, the resulting error message is:  `ValueError: None values not supported.`

The backtrace leads me through `optree/ops.py`.

It's not clear from the message what the `None` values are referring to, nor does the backtrace give me any useful information (as far as I can tell) about the source of the error. I posted this issue here: https://stackoverflow.com/q/79236884/1613983

If the error message could instead make reference to a missing labels set, null `y_true`, etc. that would make it much easier to figure out what's going on.

Somewhat ironically, I ended up solving it after asking GPT o1 about the problem, and it got the answer in one go.

### Standalone code to reproduce the issue

```shell
from tensorflow.keras import layers, Model, Input
from tensorflow.keras.models import Sequential
import pandas as pd
import numpy as np
import tensorflow as tf

input_data = pd.DataFrame(np.random.rand(1000, 10))

data = tf.data.Dataset.zip({'input_values' : tf.data.Dataset.from_tensor_slices(input_data.values)})

batch_size = 100
train_split = 0.8

train_rows = int(train_split * input_data.shape[0])
train_dataset = data.take(train_rows)
validation_dataset = data.skip(train_rows)

train_data_batched = train_dataset.batch(batch_size).prefetch(tf.data.AUTOTUNE)
validation_data_batched = validation_dataset.batch(batch_size).prefetch(tf.data.AUTOTUNE)

num_outputs = 10

input_layer = Input(shape=(num_outputs,), name=f'input_values')
output = layers.Dense(num_outputs, activation='sigmoid', name='output')(input_layer)

# Define the model
model = Model(
    inputs=[input_layer],
    outputs=output,
)

max_epochs = 10

def loss(y_true, y_pred):
    return 2.0

model.compile(
    loss=loss,
    optimizer='adam',
)

history = model.fit(
    train_data_batched,
    epochs=max_epochs,
    validation_data=validation_data_batched
)
```


### Relevant log output

```shell
File /tmp/virtualenvs/python3.11/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs)
    119     filtered_tb = _process_traceback_frames(e.__traceback__)
    120     # To get the full stack trace, call:
    121     # `keras.config.disable_traceback_filtering()`
--> 122     raise e.with_traceback(filtered_tb) from None
    123 finally:
    124     del filtered_tb

File /tmp/virtualenvs/python3.11/lib/python3.11/site-packages/optree/ops.py:752, in tree_map(func, tree, is_leaf, none_is_leaf, namespace, *rests)
    750 leaves, treespec = _C.flatten(tree, is_leaf, none_is_leaf, namespace)
    751 flat_args = [leaves] + [treespec.flatten_up_to(r) for r in rests]
--> 752 return treespec.unflatten(map(func, *flat_args))

ValueError: None values not supported.
```