Hi ,

I am trying to use amplitude embedding to encode 4 features as follows ,

```
from sklearn.datasets import load_iris
from sklearn.utils import shuffle
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :] # we only take the first two features.
Y = iris.target
trainX, testX, trainy, testy = train_test_split(X, Y, test_size=0.3, random_state=42)
trainy = tf.one_hot(trainy, depth=3)
testy = tf.one_hot(testy, depth=3)
n_qubits = 2
layers = 1
data_dimension = 3
dev = qml.device("default.qubit", wires=n_qubits)
@qml.qnode(dev)
def qnode(inputs, weights):
qml.templates.AmplitudeEmbedding(features=inputs, wires=range(n_qubits),normalize=True)
qml.templates.StronglyEntanglingLayers(weights, wires=range(n_qubits))
return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)]
weight_shapes = {"weights": (layers,n_qubits,3)}
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(n_qubits,activation='relu',input_dim=4))
model.add(qml.qnn.KerasLayer(qnode, weight_shapes, output_dim=n_qubits))
model.add(tf.keras.layers.Dense(data_dimension, activation='softmax'))
opt = tf.keras.optimizers.Adam(learning_rate=0.01)
model.compile(loss='categorical_crossentropy', optimizer=opt,metrics=["accuracy"])
history = model.fit(trainX, trainy, validation_data=(trainX, trainy), epochs=30, batch_size=5)
```

I get error while model.fit(), ValueError: ‘features’ must be of shape (4,); got (2,). Use the ‘pad’ argument for automated padding.

I have four features , number of wires are 2 , so why padding is required.

If I put pad=0.

I get , AttributeError: ‘float’ object has no attribute ‘val’

Any suggestion ?