[2, 1, 1]]

# `condition` tensor is [[[1.5 + 0.0j, 0.0  + 0.0j]
#                     [0.0 + 0.5j, 0.0  + 0.0j]]
#                    [[0.0 + 0.0j, 0.25 + 1.5j]
#                     [0.0 + 0.0j, 0.75 + 0.0j]]
#                    [[0.0 + 0.0j, 0.0  + 0.0j]
#                     [0.0 + 0.0j, 0.01 + 0.0j]]]
# 'input' has 5 nonzero magnitude values, so output has 5 coordinates.

    *   The `TextVectorization` layer now accounts for the mask_token as part of
        the vocabulary size when output_mode='int'. This means that, if you have
        a max_tokens value of 5000, your output will have 5000 unique values
        (not 5001 as before).
    *   Change the return value of `TextVectorization.get_vocabulary()` from
        `byte` to `string`. Users who previously were calling 'decode' on the