`rate` must be a scalar or scalar tensor. Received: rate=ListWrapper([3, 4, 5, 6])
As per my knowledge  you are encountering this error because you are not providing a scalar value, instead you are providing a list of values and also note that the rate value you provide must be between 0 and 1 , so in this line either change
layer = keras.layers.SpatialDropout2D(rate=[3, 4, 5, 6])  into this layer = keras.layers.SpatialDropout2D(rate=a value b/w 0 and 1) or apply the dropout for each feature map seperately Hi @j**ikechao** ,

The issue you're facing arises from the way you're defining the SpatialDropout2D layer. The error specifically mentions that the "rate" must be a scalar or scalar tensor, yet you're passing a list of values as the rate argument when initializing the SpatialDropout2D layer. This error is due to the invalid specification of the "rate" parameter. And you should take the rate(values between 0 to 1). Here I attached a [gist](https://colab.research.google.com/drive/1NnUptIW0t_ApHo2IJQDouAfOtxcGNGjI?authuser=0#scrollTo=4XIXUVodyPI9) for your reference.

Thank you! @Venkat6871 @KrishChalana Thanks for your detailed explanation. Indeed, this crash was caused by the wrong usage. 
Would you add an assertion to reject calls to this API?
BTW, Would you add an assertion to reject such unreasonable API calls when executing the statements `keras.layers.SpatialDropout2D(rate=[3, 4, 5, 6])`? Honestly, it is also acceptable to check the validity when saving the model. Hope it is what you are looking for @jikechao  
```
import tensorflow as tf
from tensorflow import keras as keras
from tensorflow.keras import layers, models
import numpy as np

def create_spatial_dropout_layer(rate):
    # Check if rate is a list of valid dropout rates
    if isinstance(rate, list) and all(isinstance(r, (int, float)) and 0 <= r <= 1 for r in rate):
        return keras.layers.SpatialDropout2D(rate=rate)
    else:
        raise ValueError("Invalid dropout rate. The rate should be a list of float values between 0 and 1.")

input_shape = [12, 10, 6, 18]
input_data = np.random.random(input_shape)

# Create the spatial dropout layer with the specified rates
dropout_layer = create_spatial_dropout_layer(rate=[0.3, 0.4, 0.5, 0.6])

x = layers.Input(shape=input_shape[1:], dtype="float32") 
y = dropout_layer(x)
model = models.Model(x, y)
model.summary()
res_keras = model(input_data)
tf.saved_model.save(model, "tf_model")

``` Thanks. Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62304">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62304">No</a>

### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

binary

### TensorFlow version

tf 2.14.0

### 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?

`tf.saved_model.save` API　can not convert the model with `SpatialDropout2D` operator and threw a crash message: "
ValueError: `rate` must be a scalar or scalar tensor. Received: rate=ListWrapper([3, 4, 5, 6])"

However, for models containing other operators, The script can run well.


### Standalone code to reproduce the issue

```shell
import tensorflow as tf
from tensorflow import keras as keras
from tensorflow.keras import layers, models
import numpy as np

layer = keras.layers.SpatialDropout2D(rate=[3, 4, 5, 6])
input_shape = [12, 10, 6, 18]
input_data = np.random.random(input_shape)
weights = layer.get_weights()
layer.set_weights(weights)

x = layers.Input(shape=input_shape[1:], dtype="float32") 
y = layer(x)
model = models.Model(x, y)
model.summary()
res_keras = model(input_data)
tf.saved_model.save(model, "tf_model")
```
```


### Relevant log output

```shell
Traceback (most recent call last):
  File "test.py", line 17, in <module>
    tf.saved_model.save(model, "tf_model")
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\saved_model\save.py", line 1331, in save
    save_and_return_nodes(obj, export_dir, signatures, options)
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\saved_model\save.py", line 1366, in save_and_return_nodes
    _build_meta_graph(obj, signatures, options, meta_graph_def))
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\saved_model\save.py", line 1578, in _build_meta_graph
    return _build_meta_graph_impl(obj, signatures, options, meta_graph_def)
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\saved_model\save.py", line 1490, in _build_meta_graph_impl
    signatures = signature_serialization.find_function_to_export(
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\saved_model\signature_serialization.py", line 109, in find_function_to_export
    for name, child in children:
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\saved_model\save.py", line 189, in list_children
    for name, child in super(_AugmentedGraphView, self).list_children(
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\checkpoint\graph_view.py", line 75, in list_children
    for name, ref in super(ObjectGraphView,
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\checkpoint\trackable_view.py", line 84, in children
    for name, ref in obj._trackable_children(save_type, **kwargs).items():
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\engine\functional.py", line 460, in _trackable_children
    dependencies.update(super()._trackable_children(save_type, **kwargs))
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\engine\training.py", line 4002, in _trackable_children
    children = super()._trackable_children(save_type, **kwargs)
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\engine\base_layer.py", line 3470, in _trackable_children
    children = self._trackable_saved_model_saver.trackable_children(
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\base_serialization.py", line 61, in trackable_children
    children = self.objects_to_serialize(serialization_cache)
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\layer_serialization.py", line 79, in objects_to_serialize
    return self._get_serialized_attributes(
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\layer_serialization.py", line 106, in _get_serialized_attributes
    object_dict, function_dict = self._get_serialized_attributes_internal(
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\model_serialization.py", line 57, in _get_serialized_attributes_internal
    objects, functions = super()._get_serialized_attributes_internal(
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\layer_serialization.py", line 117, in _get_serialized_attributes_internal
    functions = save_impl.wrap_layer_functions(
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\save_impl.py", line 168, in wrap_layer_functions
    original_fns = _replace_child_layer_functions(layer, serialization_cache)
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\save_impl.py", line 305, in _replace_child_layer_functions
    serialized_functions = child_layer._trackable_saved_model_saver._get_serialized_attributes(  # noqa: E501
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\layer_serialization.py", line 106, in _get_serialized_attributes
    object_dict, function_dict = self._get_serialized_attributes_internal(
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\layer_serialization.py", line 117, in _get_serialized_attributes_internal
    functions = save_impl.wrap_layer_functions(
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\save_impl.py", line 223, in wrap_layer_functions
    fn.get_concrete_function()
  File "C:\software\conda\envs\torch\lib\contextlib.py", line 126, in __exit__
    next(self.gen)
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\save_impl.py", line 390, in tracing_scope
    fn.get_concrete_function(*args, **kwargs)
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\eager\polymorphic_function\polymorphic_function.py", line 1222, in get_concrete_function
    concrete = self._get_concrete_function_garbage_collected(*args, **kwargs)
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\eager\polymorphic_function\polymorphic_function.py", line 1192, in _get_concrete_function_garbage_collected
    self._initialize(args, kwargs, add_initializers_to=initializers)
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\eager\polymorphic_function\polymorphic_function.py", line 694, in _initialize
    self._concrete_variable_creation_fn = tracing_compilation.trace_function(
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\eager\polymorphic_function\tracing_compilation.py", line 178, in trace_function
    concrete_function = _maybe_define_function(
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\eager\polymorphic_function\tracing_compilation.py", line 284, in _maybe_define_function
    concrete_function = _create_concrete_function(
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\eager\polymorphic_function\tracing_compilation.py", line 308, in _create_concrete_function
    traced_func_graph = func_graph_module.func_graph_from_py_func(
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\framework\func_graph.py", line 1059, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\eager\polymorphic_function\polymorphic_function.py", line 597, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\save_impl.py", line 632, in wrapper
    ret = method(*args, **kwargs)
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\utils.py", line 190, in wrap_with_training_arg
    return control_flow_util.smart_cond(
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\utils\control_flow_util.py", line 108, in smart_cond
    return tf.__internal__.smart_cond.smart_cond(
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\framework\smart_cond.py", line 53, in smart_cond
    return true_fn()
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\utils.py", line 192, in <lambda>
    lambda: replace_training_and_call(True),
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\utils.py", line 188, in replace_training_and_call
    return wrapped_call(*new_args, **new_kwargs)
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\save_impl.py", line 698, in call_and_return_conditional_losses
    call_output = layer_call(*args, **kwargs)
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\layers\regularization\dropout.py", line 120, in call
    output = control_flow_util.smart_cond(
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\utils\control_flow_util.py", line 108, in smart_cond
    return tf.__internal__.smart_cond.smart_cond(
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\framework\smart_cond.py", line 53, in smart_cond
    return true_fn()
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\layers\regularization\dropout.py", line 116, in dropped_inputs
    return self._random_generator.dropout(
  File "C:\software\conda\envs\torch\lib\site-packages\keras\src\backend.py", line 2172, in dropout
    return tf.nn.dropout(
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\util\traceback_utils.py", line 153, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 5786, in _dropout
    raise ValueError(
ValueError: `rate` must be a scalar or scalar tensor. Received: rate=ListWrapper([3, 4, 5, 6])
```
