Hello.

I am having some problems creating a keras model with a quantum layer. I obtain the following Warning when I try to train my model:

**WARNING:tensorflow:You are casting an input of type complex128 to an incompatible dtype float32. This will discard the imaginary part and may not be what you intended.**

My code is the following one:

```
import pennylane as qml
import sklearn.datasets
from qiskit_machine_learning import datasets
import qiskit_machine_learning as qiskitml
from sklearn.model_selection import train_test_split
n_qubits = 5
dev = qml.device("default.qubit", wires=n_qubits)
@qml.qnode(dev)
def qnode(inputs, weights):
qml.templates.AngleEmbedding(np.pi*inputs, wires=range(n_qubits))
qml.templates.StronglyEntanglingLayers(weights, wires=range(n_qubits))
return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)]
shape = qml.StronglyEntanglingLayers.shape(n_layers=1, n_wires=5)
weight_shapes = {"weights": shape}
inputs = np.random.rand(n_qubits).astype(comp_dtype)
weights = np.random.rand(2, n_qubits, 3).astype(comp_dtype)
X_train, X_test, Y_train, Y_test =train_test_split(x, y,test_size=0.15, random_state=0)
X = tf.constant(X_train,dtype=comp_dtype)
Y = tf.constant(Y_train,dtype=comp_dtype)
X_test= tf.constant(X_test,dtype=comp_dtype)
Y_test= tf.constant(Y_test,dtype=comp_dtype)
print(qnode(X_train[0],weights))
q_layer = qml.qnn.KerasLayer(qnode, weight_shapes, output_dim=n_qubits, dtype=comp_dtype)
q_layer.build(2)
q_model = tf.keras.models.Sequential()
q_model.add(tf.keras.layers.Dense(n_qubits, activation='sigmoid', input_dim=5))
q_model.add(q_layer)
q_model.add(tf.keras.layers.Dense(1, activation='softmax'))
q_model.summary()
opt = tf.keras.optimizers.Adam(learning_rate=0.05)
q_model.compile(loss='huber', optimizer=opt, metrics=["accuracy"])
q_model.fit(X, Y, epochs=8, batch_size=5, verbose=1, validation_data=(X_test, Y_test))
```

And my versions of tensorflow and pennylane are the following ones:

tensorflow == Version: 2.12.0

Pennylane == Version: 0.29.1

The problem is that this warning appears many times, so if someone can help me solving it or just ignoring it would be perfect.

Thanks in advance.