// function attribute prefixed with `"indy_lstm_"`.
  // IndyLSTMs have diagonal recurrent weight matrices and can benefit from
  // more efficent operations in TFLite with the correct conversion (i.e. when
  // the diagonal recurrent weight matrices are provided as vectors).
  if (attr.getValue().starts_with("indy_lstm_")) {
    // Check if the keras lstm can be fused, if not, we just don't do anything.

                                     &recurrent_weights_array)))
    return failure();

  // Reshape recurrent weights to vectors if indy behaviour is enabled.
  // IndyLSTMs are a LSTM variant with diagonal recurrent weight
  // matrices. For optimization purposes these are provided as vectors.
  Value recurrent_to_input_weights =
      indy ? mlir::cast<Value>(

    // Used in post-training dynamic range quantization. If the value is true,
    // input activations are asymmetrically quantized.
    OptionalAttr<BoolAttr>:$asymmetric_quantize_inputs,
    // IndyLSTM optimizations (i.e. optimizations enabled because of diagonal
    // recurrent weight matrices that are provided as vectors)
    OptionalAttr<BoolAttr>:$diagonal_recurrent_tensors,

    avoid leaking memory).
*   Support different summary and checkpoint directories in
    `tf.train.MonitoredTrainingSession()`.
*   Added IndRNN, IndyGRU, and IndyLSTM cells to `tf.contrib.rnn`.
*   Add safe static factory functions for SparseTensor and convert all CHECKs to
    DCHECKs. Using the constructor directly is unsafe and deprecated.