title: How to concatenate + tokenize + pad strings in TFX preprocessing?
body: <p>I'd like to perform the usual text preprocessing steps in a TensorFlow Extended pipeline's Transform step/component. My data is the following (strings in independent features, 0/1 integers in label column):</p>
  <pre><code>field1 field2 field3 label
  --------------------------
  aa     bb     cc     0
  ab     gfdg   ssdg   1 
  </code></pre>
  <p>import tensorflow as tf
  import tensorflow_text as tf_text
  from tensorflow_text import UnicodeCharTokenizer</p>
  <pre><code>def preprocessing_fn(inputs):
      
      outputs = {}
      outputs['features_xf'] = tf.sparse.concat(axis=0, sp_inputs=[inputs[&quot;field1&quot;], inputs[&quot;field2&quot;], inputs[&quot;field3&quot;]])
      outputs['label_xf'] = tf.convert_to_tensor(inputs[&quot;label&quot;], dtype=tf.float32)
  
      return outputs
  </code></pre>
  <p>but this doesn't work:</p>
  <pre><code>ValueError: Arrays were not all the same length: 3 vs 1 [while running 'Transform[TransformIndex0]/ConvertToRecordBatch']
  </code></pre>
  <p>(Later on I want to apply char-level tokenization and padding to <code>MAX_LEN</code> as well).<br />
  Any idea?</p>