Fix dtype and assign* in AutocastVariable.

The `dtype` property would return to true dtype of the variable, instead of the dtype of the value that you get explicitly via `.value()` or implicitly by doing any operation.

This would cause seemingly correct things like this to fail with a dtype mismatch:
```
y = variable * tf.cast(x, variable.dtype)
```

Forcing users to write workarounds like:
```
v = variable.value()
y = variable * tf.cast(x, v.dtype)
```

Additionally, `assign`, `assign_add`, `assign_sub` expected the value to be of the true dtype, not the cast dtype.

This would cause seemingly correct things like this to fail with a dtype mismatch:
```
variable.assign(variable * factor)
```
(This is a common use case for non-trainable variables.)

Forcing users to write workarounds like:
```
variable.assign(tf.cast(variable * factor, variable.dtype))
```

This changes fixes these issues to make autocasting fully transparent:
- `dtype` returns the cast dtype if applicable
- `assign*` accept the cast dtype for the value if applicable

Note that this is consistent with how autocasting works in Keras 3.

PiperOrigin-RevId: 650386711

Author: hertschuh

tensorflow_model_optimization/python/core/sparsity/keras/pruning_wrapper.py

@@ -233,19 +233,25 @@ def build(self, input_shape):
 
     # For each of the prunable weights, add mask and threshold variables
     for weight in self.prunable_weights:
+      # Under a mixed precision policy, variables report their "cast" dtype.
+      # However, we want to use the original dtype for mask and threshold.
+      if hasattr(weight, 'true_dtype'):
+        dtype = weight.true_dtype
+      else:
+        dtype = weight.dtype
       mask = self.add_weight(
           'mask',
           shape=weight.shape,
           initializer=keras.initializers.get('ones'),
-          dtype=weight.dtype,
+          dtype=dtype,
           trainable=False,
           aggregation=tf.VariableAggregation.MEAN,
       )
       threshold = self.add_weight(
           'threshold',
           shape=[],
           initializer=keras.initializers.get('zeros'),
-          dtype=weight.dtype,
+          dtype=dtype,
           trainable=False,
           aggregation=tf.VariableAggregation.MEAN,
       )