Incompatible Shapes?

I’m trying to build a two input model with keras, with each input being a string.

Here’s the code for the model

    vectorize_layer1 = TextVectorization(split="character", output_sequence_length=512,

    # define two sets of inputs
    inputA = Input(shape=(1,), dtype=tf.string)
    inputB = Input(shape=(1,), dtype=tf.string)

    # the first branch operates on the first input
    x = vectorize_layer1(inputA)
    x = Embedding(len(vectorize_layer1.get_vocabulary()), MAX_STRING_SIZE)(x)
    x = Bidirectional(LSTM(MAX_STRING_SIZE, return_sequences=True, dropout=.2))(x)
    x = LSTM(MAX_STRING_SIZE, activation="tanh", return_sequences=False, dropout=.2)(x)
    x = Model(inputs=inputA, outputs=x)

    # the second branch opreates on the second input
    y = vectorize_layer1(inputB)
    y = Embedding(len(vectorize_layer1.get_vocabulary()), MAX_STRING_SIZE)(y)
    y = Bidirectional(LSTM(MAX_STRING_SIZE, return_sequences=True, dropout=.2))(y)
    y = LSTM(MAX_STRING_SIZE, activation="tanh", return_sequences=False, dropout=.2)(y)
    y = Model(inputs=inputB, outputs=y)

    # combine the output of the two branches
    combined = concatenate([x.output, y.output])
    # apply a FC layer and then a prediction on the categories
    z = Dense(2, activation="relu")(combined)
    z = Dense(len(LABELS), activation="softmax")(z)

    # our model will accept the inputs of the two branches an
    model = Model(inputs=[x.input, y.input], outputs=z)
    print(model.predict((np.array(["i love python"]), np.array(["test"]))))
    #That prediction works fine! 

    plot_model(model, to_file="model.png", show_shapes=True, show_layer_names=True)

I’m getting the Shapes are incompatible error though:

 line 5119, in categorical_crossentropy

    ValueError: Shapes (None, 1) and (None, 20) are incompatible

Here is an example of the training/validation data:

((array(['foo'], dtype='<U6'), array(['bar'], dtype='<U26')), array([0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]))

Any ideas what is going wrong? The data looks ok to me, the categories are 1-hot encoded.

Model diagram

may be this loss function can fix this issue ```
replace this loss function with yours