tensorflow: OperatorNotAllowedInGraphError: using a tf.Tensor as a Python bool is not allowed in Graph execution. Use Eager execution or decorate this function with @tf.function.

def unet(pretrained_weights = None,input_size = (256,256,1)):
    inputs = keras.Input(shape = input_size)
    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
    conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

    conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
    conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
    drop5 = Dropout(0.5)(conv5)

    up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
    merge6 = concatenate([drop4,up6], axis = 3)
    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)

    up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
    merge7 = concatenate([conv3,up7], axis = 3)
    conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
    conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)

    up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
    merge8 = concatenate([conv2,up8], axis = 3)
    conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
    conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)

    up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
    merge9 = concatenate([conv1,up9], axis = 3)
    conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
    conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
    conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
    conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)

    model = Model(inputs = inputs, outputs = conv10)

    def iou(y_pred, y_true):
        y_pred = tf.cast((y_pred > 0), dtype=tf.float32)
        i = tf.reduce_sum(y_true * y_pred)
        u = tf.reduce_sum(y_true + y_pred)
        return (i / u).item()if u != 0 else u.item()
    
    model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy',iou])
    
    model.summary()


    if(pretrained_weights):
    	model.load_weights(pretrained_weights)

    return `model`

model = unet()

When I run the code above, I encounter the following errors:

OperatorNotAllowedInGraphError Traceback (most recent call last)
in 
----> 1 model = unet()

in unet(pretrained_weights, input_size)
51 return (i / u).item()if u != 0 else u.item()
52
---> 53 model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy',iou])
54
55 model.summary()

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\training\tracking\base.py in _method_wrapper(self, *args, **kwargs)
455 self._self_setattr_tracking = False # pylint: disable=protected-access
456 try:
--> 457 result = method(self, *args, **kwargs)
458 finally:
459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\engine\training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, distribute, **kwargs)
437 targets=self._targets,
438 skip_target_masks=self._prepare_skip_target_masks(),
--> 439 masks=self._prepare_output_masks())
440
441 # Prepare sample weight modes. List with the same length as model outputs.

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\engine\training.py in _handle_metrics(self, outputs, targets, skip_target_masks, sample_weights, masks, return_weighted_metrics, return_weighted_and_unweighted_metrics)
2002 metric_results.extend(
2003 self._handle_per_output_metrics(self._per_output_metrics[i],
-> 2004 target, output, output_mask))
2005 if return_weighted_and_unweighted_metrics or return_weighted_metrics:
2006 metric_results.extend(

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\engine\training.py in _handle_per_output_metrics(self, metrics_dict, y_true, y_pred, mask, weights)
1953 with K.name_scope(metric_name):
1954 metric_result = training_utils.call_metric_function(
-> 1955 metric_fn, y_true, y_pred, weights=weights, mask=mask)
1956 metric_results.append(metric_result)
1957 return metric_results

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\engine\training_utils.py in call_metric_function(metric_fn, y_true, y_pred, weights, mask)
1153
1154 if y_pred is not None:
-> 1155 return metric_fn(y_true, y_pred, sample_weight=weights)
1156 # Mean metric only takes a single value.
1157 return metric_fn(y_true, sample_weight=weights)

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\metrics.py in call(self, *args, **kwargs)
194 from tensorflow.python.keras.distribute import distributed_training_utils # pylint:disable=g-import-not-at-top
195 return distributed_training_utils.call_replica_local_fn(
--> 196 replica_local_fn, *args, **kwargs)
197
198 @Property

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\distribute\distributed_training_utils.py in call_replica_local_fn(fn, *args, **kwargs)
1133 with strategy.scope():
1134 return strategy.extended.call_for_each_replica(fn, args, kwargs)
-> 1135 return fn(*args, **kwargs)
1136
1137

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\metrics.py in replica_local_fn(*args, **kwargs)
177 def replica_local_fn(*args, **kwargs):
178 """Updates the state of the metric in a replica-local context."""
--> 179 update_op = self.update_state(*args, **kwargs) # pylint: disable=not-callable
180 with ops.control_dependencies([update_op]):
181 result_t = self.result() # pylint: disable=not-callable

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\utils\metrics_utils.py in decorated(metric_obj, *args, **kwargs)
74
75 with tf_utils.graph_context_for_symbolic_tensors(*args, **kwargs):
---> 76 update_op = update_state_fn(*args, **kwargs)
77 if update_op is not None: # update_op will be None in eager execution.
78 metric_obj.add_update(update_op)

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\metrics.py in update_state(self, y_true, y_pred, sample_weight)
585 y_pred, y_true)
586
--> 587 matches = self._fn(y_true, y_pred, **self._fn_kwargs)
588 return super(MeanMetricWrapper, self).update_state(
589 matches, sample_weight=sample_weight)

in iou(y_pred, y_true)
49 i = tf.reduce_sum(y_true * y_pred)
50 u = tf.reduce_sum(y_true + y_pred)
---> 51 return (i / u).item()if u != 0 else u.item()
52
53 model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy',iou])

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\framework\ops.py in bool(self)
755 TypeError.
756 """
--> 757 self._disallow_bool_casting()
758
759 def nonzero(self):

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\framework\ops.py in _disallow_bool_casting(self)
524 else:
525 # Default: V1-style Graph execution.
--> 526 self._disallow_in_graph_mode("using a tf.Tensor as a Python bool")
527
528 def _disallow_iteration(self):

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\framework\ops.py in _disallow_in_graph_mode(self, task)
513 raise errors.OperatorNotAllowedInGraphError(
514 "{} is not allowed in Graph execution. Use Eager execution or decorate"
--> 515 " this function with @tf.function.".format(task))
516
517 def _disallow_bool_casting(self):

OperatorNotAllowedInGraphError: using a tf.Tensor as a Python bool is not allowed in Graph execution. Use Eager execution or decorate this function with @tf.function.

How can I correct my code so that I can run it successfully?