While running a custom Keras model with tensorboard callback. The Conceptual graph is generated, however, the Op graph returns: `Error: Malformed GraphDef`

. I tried some existing suggestions related to potential naming conflicts and using `name_scope`

, however, to no avail.

```
logdir = 'logs/func/' + datetime.now().strftime("%Y%m%d-%H%M%S")
class MCLayer(tf.keras.layers.Layer):
def __init__(self, name=None):
super(MCLayer, self).__init__(name=name)
#with tf.name_scope('test1'):
self.nT = tf.constant(400)
self.n = tf.constant(100000)
self.dt = tf.constant(1/365)
self.drift = tf.constant(0.08)
self.sigma = tf.constant(0.1)
#@tf.function
def call(self, inputs):
#with tf.name_scope('test2'):
dWt = tf.random.normal(mean=0, stddev=tf.math.sqrt(self.dt), shape=[self.nT, self.n])
dYt = self.drift*self.dt + self.sigma*dWt
C = tf.cumsum(dYt, axis=0)
S = tf.exp(C)
A = tf.reduce_mean(S, axis=0)
P = tf.reduce_mean(tf.maximum(A - inputs, 0))
return P
input_layer = tf.keras.layers.Input(shape=(1), name='input_layer')
output_layer = MCLayer(name='output_layer')(input_layer)
model = tf.keras.models.Model(input_layer, output_layer, name='SomeModel')
model.compile()
result = model.predict(tf.constant(1.0, shape=(1,)), callbacks=[tf.keras.callbacks.TensorBoard(log_dir=logdir)])
```