I am trying to create a keras layer for training a simple NN that would use a single qubit and covert centigrade to fahrenheit.

Code is as below , I am getting error , `'InvalidArgumentError: Input tensor must be at least 2D: [1] [Op:BiasAdd]'`

.

The disconnect is that I am not able to understand the use of `weights_shape`

. Can you explain use of `weights_shape `

with examples ?

```
import pennylane as qml
import tensorflow as tf
import numpy as np
n_qubits = 1
dev = qml.device("default.qubit", wires=n_qubits)
@qml.qnode(dev)
def qnode(inputs, weights):
qml.templates.AngleEmbedding(inputs, wires=range(n_qubits))
qml.templates.StronglyEntanglingLayers(weights, wires=range(n_qubits))
return qml.expval(qml.expval(qml.PauliZ(0)))
weight_shapes = {"weights": (1, n_qubits, 3)}
qlayer = qml.qnn.KerasLayer(qnode, weight_shapes, output_dim=1)
clayer1 = tf.keras.layers.Dense(1)
clayer2 = tf.keras.layers.Dense(1, activation="linear")
model = tf.keras.models.Sequential([clayer1, qlayer, clayer2])
X = np.array([-40, -10, 0, 8, 15, 22, 38], dtype=float)
Y = np.array([-40, 14, 32, 46, 59, 72, 100], dtype=float)
X1 = X.reshape((len(X), 1))
Y1 = Y.reshape((len(Y), 1))
opt = tf.keras.optimizers.SGD(learning_rate=0.5)
model.compile(opt, loss='mse',)
model.fit(X1, Y1, epochs=60, batch_size=1)
```