Model error: Layer model_1 expects 1 input(s), but it received 2 input tensors

Here is the model

# Convert data types from int64 to float32 to use as tensor inputs for Keras model
train_zero = tf.convert_to_tensor(train_zero, dtype=tf.float32)
train_one = tf.convert_to_tensor(train_one, dtype=tf.float32)
train_two = tf.convert_to_tensor(train_two, dtype=tf.float32)
#train_three = tf.convert_to_tensor(train_three, dtype=tf.float32)
#train_four = tf.convert_to_tensor(train_four, dtype=tf.float32)
#train_five = tf.convert_to_tensor(train_five, dtype=tf.float32)
train_average = tf.convert_to_tensor(train_average, dtype=tf.float32)
validate = tf.convert_to_tensor(validate, dtype=tf.float32)
test = tf.convert_to_tensor(test, dtype=tf.float32)



def AutoRec(X, reg, first_activation, last_activation):
  
    input_layer = x = Input(shape=(X.shape[1],), name='UserRating')
    x = Dense(500, activation=first_activation, name='LatentSpace', kernel_regularizer=regularizers.l2(reg))(x)
    output_layer = Dense(X.shape[1], activation=last_activation, name='UserScorePred', kernel_regularizer=regularizers.l2(reg))(x)
    model = Model(input_layer, output_layer)

    return model

AutoRec = AutoRec(train_zero, 0.0005, 'elu', 'elu')

AutoRec.compile(optimizer = Adam(lr=0.0001), loss=masked_mse, metrics=[masked_rmse_clip])
 
AutoRec.summary()


hist_model = AutoRec.fit(x=train_average, y=train_zero,
                  epochs=5,
                  batch_size=256,
                  verbose = 2, 
                  validation_data=(train_average, validate))

You should try to change the shape of your input layer, or to see of their is a probleme in your data. Also, I don't know if it's wanted from you but your model only have input and output layer, maybe you forgot you add your dense layer 'x' in the "AutoRec" function.

can you show us the code of your NN model and the kind of data you are giving to it ? I think that the problem is that you are giving data which is composed of 2 tensor but your model input layer is programed to only receive 1 tensor data.