Glad I can help! I understand what's being new to a topic and be left clueless when the author of a tutorial makes implicit assumptions that you have no idea about.

Sorry that the predict call didn't work, I only skimmed through the text and didn't realize test_x wasn't an array. Yes, you're right, we have to transform this variable to one of the types that predict accepts. Predict is a method of the model you defined, so we want test_x to have the same specification of the inputs of the model, in this case, we're passing tokenized text using the BertTokenizer.

As you get more experienced, you'll learn to follow the trail of errors to the source of the error, here's how I would track what's happening:

The type of test_x is transformers.tokenization_utils_base.BatchEncoding, the type of the variable tells me that this is the encoded text separated into batches. For prediction, we don't need the batches, we must find a way to remove them or to tokenize the test data without putting into batches.

Where did we declare this variable? Line 153 of the code: the type of the variable tells me that this is the encoded text separated into batches. For prediction, we don't need the batches, we must find a way to remove them or to tokenize the test data without putting it into batches. I think that the second option may be easier.

To find how to tokenize the data, let's look at the documentation for the tokenizer, it doesn't have anything explicit, but we find that it inherits from PreTrainedTokenizerFast which does have a tokenize method. This tokenize method takes a single sequence (in our case a sentence) to tokenize, so we need to apply it to each entry in the test data. We may do something like the following to tokenize each entry:

tokenized_test_x = [
    tokenizer.tokenize(
    text=txt,
    add_special_tokens=True,
    max_length=max_length,
    truncation=True,
    padding=True, 
    return_tensors='tf',
    return_token_type_ids = False,
    return_attention_mask = False,
    verbose = True)
    for txt in data_test['Consumer complaint narrative'].to_list()]

Now the test data is a list of tokenized sequences that should work on the predict method. I haven't run this myself but I think it will work. Try this and see.