// layer norm coefficients
  Value input_layer_norm_coefficients_;
  Value forget_layer_norm_coefficients_;
  Value cell_layer_norm_coefficients_;
  Value output_layer_norm_coefficients_;

  mlir::TFL::LSTMOp lstm_;

  Value none_;
  SmallVector<int64_t, 1> bias_slice_shape_;
  SmallVector<int64_t, 1> bias_size_values_;
  SmallVector<int64_t, 2> weight_slice_shape_;
  SmallVector<int64_t, 2> weight_slice_size_input_values_;

  // The func will ultimately return the output of the fused
  // LSTM op.
  UpdateFuncSignature();

  // Transform the weights, projection, bias and layer norm coefficients
  // to generate operands for the TFL fused LSTM op.
  GenerateFusedOpOperands();

  // Create the fused LSTM op.
  SmallVector<int64_t, 2> output_shape = {1, n_output_};
  auto result_type = mlir::RankedTensorType::get(

  // Keras lstm tf.api_implements usually has attribute like "lstm_abcde91...".
  // TODO(b/147436982): we need to make sure that only the
  // outputs(full sequence) is used, not the last_output, not the new_states.
  // We will discard everything except the outputs.
  // And the outputs is in the shape of [batch, time, units].
  if (attr.getValue().starts_with("lstm_")) {

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s
// Ensure lstm roundtrip exactly

// CHECK: {
// CHECK-NEXT:   version: 3,
// CHECK-NEXT:   operator_codes: [ {
// CHECK-NEXT:     deprecated_builtin_code: 16,
// CHECK-NEXT:     version: 1,
// CHECK-NEXT:     builtin_code: LSTM
// CHECK-NEXT:   } ],
// CHECK-NEXT:   subgraphs: [ {
// CHECK-NEXT:     tensors: [ {
// CHECK-NEXT:       shape: [ 1, 4 ],
// CHECK-NEXT:       buffer: 1,
// CHECK-NEXT:       name: "arg0",

  // CHECK-DAG:  %[[CST_3:.*]] = "tfl.pseudo_const"() <{value = dense<0.000000e+00> : tensor<4x4xf32>}> : () -> tensor<4x4xf32>

Christian Sigg <******@****.***> 1714543668 -0700

// block (or nil), ctxt provides information about the enclosing statements,
// and lstmt is the labeled statement associated with this block, or nil.
func (ls *labelScope) blockBranches(parent *block, ctxt targets, lstmt *LabeledStmt, start Pos, body []Stmt) []*BranchStmt {
	b := &block{parent: parent, start: start, lstmt: lstmt}

	var varPos Pos
	var varName Expr
	var fwdGotos, badGotos []*BranchStmt

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

// Transform pass for LSTMs.

#ifndef TENSORFLOW_COMPILER_MLIR_LITE_TRANSFORMS_PREPARE_QUANTIZE_HELPER_H_
#define TENSORFLOW_COMPILER_MLIR_LITE_TRANSFORMS_PREPARE_QUANTIZE_HELPER_H_

#include <algorithm>
#include <cmath>
#include <memory>

	for s := b; s != nil; s = s.parent {
		if t := s.lstmt; t != nil && t.Label.Value == name {
			return t
		}
	}
	return nil
}

// blockBranches processes a block's statement list and returns the set of outgoing forward jumps.
// all is the scope of all declared labels, parent the set of labels declared in the immediately
// enclosing block, and lstmt is the labeled statement this block is associated with (or nil).