Design feedforward NN that will use p$,$ q and r as input signals to find B$.$ The truth table is

given in Figure $4.$

import tensorflow as tf

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense

# Define the model

model $=$ Sequential$([$

Dense$(4,$ input$\_$dim$=3,$ activation$=$'relu'$),$ # Hidden layer with $4$ neurons

Dense$(1,$ activation$=$'sigmoid'$)$ # Output layer

$])$

# Compile the model

model.compile$($optimizer$=$'adam', loss$=$'binary$\_$crossentropy', metrics$=[\text{'}$accuracy$\text{'}])$

# Define the inputs and outputs

X $=[[0,0,0],[0,0,1],[0,1,0],[0,1,1],$

$[1,0,0],[1,0,1],[1,1,0],[1,1,1]]$

Y $=[0,1,1,0,1,0,0,1]$ # Corresponding outputs for B

# Train the model

model.fit$($X$,$ Y$,$ epochs$=100,$ batch$\_$size$=1)$

could you please i need the result of this bython code