title: How to go about creating embeddings (especially, token to Id mapping) for categorical columns in tensorflow 2.0+?
body: <p>I have a csv with both categorical and float dtypes. I want to do the following:</p>
  
  <ol>
  <li>For each categorical column i will use pandas to compute the unique values (<code>pd.unique()</code>) that are present in the column. say <code>u_l</code> for a column</li>
  <li>I will use the <code>len(u_l)</code> to decide upon the dimension of embeddings that i use for a particular categorical column that i want i embed (this step is the reason i cannot use tensorflow_transform)</li>
  <li>I want to create some stateful node that can map <code>category (token)</code> value to embeddings index thus subsequently i can lookup the embedding from embeddings matrix that i created in step 2</li>
  </ol>
  
  <p>I dont know how to go about doing it currently. A very inelegant solution i can see is using tensorflow_datasets:</p>
  
  <ol>
  <li><code>encoder = tfds.features.text.TokenTextEncoder(u_l,decode_token_separator=' ')</code></li>
  <li>concatenate the entire column using space delimiter (<code>c_l</code>) (<code>c_l</code> is one string now) and then using <code>encoder.encode(c_l)</code></li>
  </ol>
  
  <p>This is a very basic thing that i think tensorflow would be able to do relatively easily. Please guide me to the right solution</p>