code: def LSTMSeq2SeqAttn(
      input_vocab_size=256,
      target_vocab_size=256,
      d_model=512,
      n_encoder_layers=2,
      n_decoder_layers=2,
      n_attention_heads=1,
      attention_dropout=0.0,
      mode="train",
  ):
      """Returns an LSTM sequence-to-sequence model with attention.
  
      This model is an encoder-decoder that performs tokenized string-to-string
      ("source"-to-"target") transduction:
  
        - inputs (2):
  
            - source: rank 2 tensor representing a batch of text strings via token
              IDs plus padding markers; shape is (batch_size, sequence_length). The
              tensor elements are integers in `range(input_vocab_size)`, and `0`
              values mark padding positions.
  
            - target: rank 2 tensor representing a batch of text strings via token
              IDs plus padding markers; shape is (batch_size, sequence_length). The
              tensor elements are integers in `range(output_vocab_size)`, and `0`
              values mark padding positions.
  
        - output: rank 3 tensor representing a batch of log-probability
          distributions for each sequence position over possible token IDs;
          shape is (batch_size, sequence_length, `vocab_size`).
  
      An example use would be to translate (tokenized) sentences from English to
      German.
  
      The model works as follows:
  
      * Input encoder runs on the input tokens and creates activations that
        are used as both keys and values in attention.
      * Pre-attention decoder runs on the targets and creates
        activations that are used as queries in attention.
      * Attention runs on the queries, keys and values masking out input padding.
      * Decoder runs on the result, followed by a cross-entropy loss.
  
      Args:
        input_vocab_size: Input vocabulary size -- each element of the input tensor
            should be an integer in `range(vocab_size)`. These integers typically
            represent token IDs from a vocabulary-based tokenizer.
        target_vocab_size: Target vocabulary size.
        d_model: Final dimension of tensors at most points in the model, including
            the initial embedding output.
        n_encoder_layers: Number of LSTM layers in the encoder.
        n_decoder_layers: Number of LSTM layers in the decoder after attention.
        n_attention_heads: Number of attention heads.
        attention_dropout: Stochastic rate (probability) for dropping an activation
            value when applying dropout within an attention block.
        mode: If `'predict'`, use fast inference. If `'train'`, each attention block
            will include dropout; else, it will pass all values through unaltered.
  
      Returns:
        An LSTM sequence-to-sequence model as a layer that maps from a
        source-target tokenized text pair to activations over a vocab set.
      """
      input_encoder = tl.Serial(
          tl.Embedding(input_vocab_size, d_model),
          [tl.LSTM(d_model) for _ in range(n_encoder_layers)],
      )
  
      pre_attention_decoder = tl.Serial(
          tl.ShiftRight(mode=mode),
          tl.Embedding(target_vocab_size, d_model),
          tl.LSTM(d_model, mode=mode),
      )
  
      def PrepareAttentionInputs():
          """Layer that prepares queries, keys, values and mask for attention."""
  
          def F(encoder_activations, decoder_activations, input_tokens):
              keys = values = encoder_activations
              queries = decoder_activations
              # Mask is 1 where inputs are not padding (0) and 0 where they are padding.
              mask = input_tokens != 0
              # We need to add axes to the mask for attention heads and decoder length.
              mask = jnp.reshape(
                  mask, (mask.shape[0], 1, 1, mask.shape[1])
              )
              # Broadcast so mask is [batch, 1 for heads, decoder-len, encoder-len].
              mask = mask + jnp.zeros(
                  (1, 1, decoder_activations.shape[1], 1)
              )
              mask = mask.astype(jnp.float32)
              return queries, keys, values, mask
  
          return tl.Fn("PrepareAttentionInputs", F, n_out=4)
  
      return tl.Serial(  # in-toks, target-toks
          tl.Select(
              [0, 1, 0, 1]
          ),  # in-toks, target-toks, in-toks, target-toks
          tl.Parallel(input_encoder, pre_attention_decoder),
          PrepareAttentionInputs(),  # q, k, v, mask, target-toks
          tl.Residual(
              tl.AttentionQKV(
                  d_model,
                  n_heads=n_attention_heads,
                  dropout=attention_dropout,
                  mode=mode,
                  cache_KV_in_predict=True,
              )
          ),  # decoder-vecs, mask, target-toks
          tl.Select([0, 2]),  # decoder-vecs, target-toks
          [
              tl.LSTM(d_model, mode=mode)
              for _ in range(n_decoder_layers)
          ],
          tl.Dense(target_vocab_size),
          tl.LogSoftmax(),
      )
  
imports: from trax import layers
  from trax.fastmath import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: LSTMSeq2SeqAttn(input_vocab_size, target_vocab_size, d_model, n_encoder_layers, n_decoder_layers, n_attention_heads, attention_dropout, mode) and returns: tl, tl, queries,  keys,  values,  mask