Hello!

I’ve tried to convert a qnode to a keras layer and to integrate this **kerasLayer**

to `tf.keras.models.Sequential`

model

I got this error

`TypeError: RY: Real scalar parameter expected, got <class 'numpy.ndarray'>.`

.

```
@qml.qnode(dev)
def circuit(inputs, conv_params):
# Encoding of 4 classical input values
for j in range(4):
qml.RY(np.pi * inputs[j], wires=j)
# Random quantum circuit
RandomLayers(conv_params, wires=list(range(4)))
# Measurement producing 4 classical output values
return [qml.expval(qml.PauliZ(j)) for j in range(4)]
qlayer = qml.qnn.KerasLayer(circuit, weight_shapes, output_dim=4)
def MyModel():
"""Initializes and returns a custom Keras model
which is ready to be trained."""
model = keras.models.Sequential([
qlayer,
keras.layers.Conv2D(64, (3, 3), activation='relu'),
keras.layers.MaxPooling2D((2, 2)),
keras.layers.Conv2D(64, (3, 3), activation='relu'),
keras.layers.MaxPooling2D((2, 2)),
keras.layers.Flatten(),
keras.layers.Dense(10, activation="softmax")
])
model.compile(
optimizer='adam',
loss="sparse_categorical_crossentropy",
metrics=["accuracy"],
)
return model
q_model = MyModel()
q_history = q_model.fit(
train_images,
train_labels,
validation_data=(test_images, test_labels),
batch_size=4,
epochs=n_epochs,
verbose=2,
)
```

Can anyone help me please!