I am trying to implement the following code:

import numpy as np

import tensorflow as tf

class SimpleDense(tf.keras.layers.Layer):

def **init**(self, units):

super(SimpleDense, self).**init**()

self.units = units

```
def build(self, input_shape):
w_init = tf.random_normal_initializer()
self.w = tf.Variable(name = "kernel", initial_value = w_init(shape = (input_shape[-1], self.units), dtype = 'float32'), trainable = True)
b_init = tf.zeros_initializer()
self.b = tf.Variable(name = "bias", initial_value = b_init(shape = (self.units,), dtype = 'float32'), trainable = True)
def call(self, inputs):
return tf.matmul(inputs, self.w) + self.b
```

x = np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], dtype = float)

y = np.array([-3.0, -1.0, 1.0, 3.0, 5.0, 7.0], dtype = float)

model = tf.keras.models.Sequential([SimpleDense(units = 1)])

model.compile(optimizer = ‘adam’, loss = ‘mean_squared_error’)

model.fit(x, y, epochs = 500, verbose = 0)

print(model.predict([10.0]))

But I keep getting the following error:

```
ValueError: Exception encountered when calling layer "sequential_7" (type Sequential).
Attempt to convert a value (None) with an unsupported type (<class 'NoneType'>) to a Tensor.
Call arguments received by layer "sequential_7" (type Sequential):
• inputs=tf.Tensor(shape=(None,), dtype=float32)
• training=True
• mask=None
```