What are the key parameters to customize Bahdanau attention in TensorFlow

1. `units`: The depth of the query mechanism. This is the number of units in the attention mechanism's output.

2. `memory`: The memory to query; usually the output of an RNN encoder. This tensor should be shaped `[batch_size, max_time, ...]`.

3. **`memory_sequence_length``: (Optional) Sequence lengths for the batch entries in memory. If provided, the memory tensor rows are masked with zeros for values past the respective sequence lengths.

4. `normalize`: Python boolean. Whether to normalize the energy term.

5. `probability_fn`: (Optional) String, the name of function to convert the attention score to probabilities. The default is `softmax` which is `tf.nn.softmax`. Other options include `hardmax`, which is `tf.nn.softmax` within this module. Any other value will result in a validation error.

6. `kernel_initializer`: (Optional) The name of the initializer for the attention kernel.

7. `dtype`: The data type for the query and memory layers of the attention mechanism.

8. `name`: Name to use when creating ops.

9. `kwargs`**: Dictionary that contains other common arguments for layer creation.

Here is an example of how to use these parameters:

python
attnUnits = 128
attention = BahdanauAttention(
    units=attnUnits,
    memory=encoderOutput,
    memory_sequence_length=encoderSequenceLength,
    normalize=True,
    probability_fn='softmax',
    kernel_initializer='glorot_uniform',
    dtype=tf.float32,
    name='BahdanauAttention'
)