import tensorflow as tf

                              input_dtype,
                              output0_dtype,
                              output1_dtype,
                              swap=False):

    if not tu.validate_for_tf_model(input_dtype, output0_dtype, output1_dtype,
                                    input_shape, output0_shape, output1_shape):
        return

    tf_input_dtype = np_to_tf_dtype(input_dtype)
    tf_output0_dtype = np_to_tf_dtype(output0_dtype)
    tf_output1_dtype = np_to_tf_dtype(output1_dtype)

    # Create the model. If non-batching then don't include the batch
    # dimension.
    tf.reset_default_graph()
    if max_batch == 0:
        in0 = tf.placeholder(tf_input_dtype, tu.shape_to_tf_shape(input_shape),
                             "INPUT0")
        in1 = tf.placeholder(tf_input_dtype, tu.shape_to_tf_shape(input_shape),
                             "INPUT1")
    else:
        in0 = tf.placeholder(tf_input_dtype, [
            None,
        ] + tu.shape_to_tf_shape(input_shape), "INPUT0")
        in1 = tf.placeholder(tf_input_dtype, [
            None,
        ] + tu.shape_to_tf_shape(input_shape), "INPUT1")

    # If the input is a string, then convert each string to the
