I'd like to change the Keras to a pytorch, but I don't know how to build a neural network

Question

The HAR dataset should be analyzed using LSTM and 1D CNN. I need to check the graph of the change in loss and check the confusion matrix. I don't know how to make init and forward functions in pytorch....

# define model
model = Sequential()
model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', input_shape=(n_steps, 1, n_length, n_features)))
model.add(Dropout(0.1))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(n_outputs, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# fit network
hist = model.fit(X_train, Y_train, epochs=epochs, validation_data=(X_test, Y_test), batch_size=batch_size, verbose=verbose)

# evaluate model
(loss, accuracy) = model.evaluate(X_test, Y_test, batch_size=batch_size, verbose=verbose)
print("[INFO] loss={:.4f}, accuracy: {:.4f}%".format(loss, accuracy * 100))

The above is an LSTM model implemented by keras.

model = Sequential()
model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps,n_features)))
model.add(Conv1D(filters=64, kernel_size=3, activation='relu', padding = 'same'))
model.add(Dropout(0.3))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(n_outputs, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# fit network
history = model.fit(X_train, Y_train, validation_data=(X_test, Y_test), 
                    epochs=epochs, batch_size=batch_size, callbacks = [checkpoint], verbose=verbose)


# evaluate model
(loss, accuracy) = model.evaluate(X_test, Y_test, batch_size=batch_size, verbose=verbose)
print("[INFO] loss={:.4f}, accuracy: {:.4f}%".format(loss, accuracy * 100))

The above is a 1D CNN model implemented by keras.

I started deep learning a few months ago, so I don't know. Help me.