Write a tensorflow code that trains two neural networks on two datasets, but teh weights of the last layer are shared between the two
 Sure! Here is an example of how you can train two neural networks with shared weights for the last layer using TensorFlow and Keras:
```
import tensorflow as tf
from tensorflow.keras.layers import Dense, Input
from tensorflow.keras.models import Model

# Define the shared layer
shared_layer = Dense(10, activation='relu')

# Define the two input layers
input_1 = Input(shape=(8,))
input_2 = Input(shape=(8,))

# Pass the input layers through their own unique layers
x1 = Dense(64, activation='relu')(input_1)
x2 = Dense(64, activation='relu')(input_2)

# Share the weights for the last layer
x1 = shared_layer(x1)
x2 = shared_layer(x2)

# Define the two models
model_1 = Model(inputs=input_1, outputs=x1)
model_2 = Model(inputs=input_2, outputs=x2)

# Compile the models
model_1.compile(optimizer='adam', loss='mse')
model_2.compile(optimizer='adam', loss='mse')

# Train the models on their respective datasets
model_
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