// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package norm

type valueRange struct {
	value  uint16 // header: value:stride
	lo, hi byte   // header: lo:n
}

type sparseBlocks struct {
	values []valueRange
	offset []uint16
}

var nfcSparse = sparseBlocks{
	values: nfcSparseValues[:],
	offset: nfcSparseOffset[:],
}

  }
  builder.setInsertionPoint(cluster_func_op);
  auto xla_launch_op = builder.create<TF::XlaLaunchOp>(
      cluster_func_op.getLoc(), cluster_func_op.getResultTypes(),
      /*constants=*/ValueRange({}), ValueRange(non_resource_args),
      ValueRange(resource_args), cluster_func_op.getFuncAttr());

  CopyDeviceAndUnderscoredAttributes(cluster_func_op, xla_launch_op);
  cluster_func_op.replaceAllUsesWith(xla_launch_op.getResults());

var sparse = sparseBlocks{
	values:  sparseValues[:],
	offsets: sparseOffsets[:],
}

// Sparse block lookup code.

// valueRange is an entry in a sparse block.
type valueRange struct {
	value  uint16
	lo, hi byte
}

type sparseBlocks struct {
	values  []valueRange
	offsets []uint16
}

// lookup returns the value from values block n for byte b using binary search.

#include "mlir/IR/IRMapping.h"  // from @llvm-project
#include "mlir/IR/Operation.h"  // from @llvm-project
#include "mlir/IR/PatternMatch.h"  // from @llvm-project
#include "mlir/IR/Value.h"  // from @llvm-project
#include "mlir/IR/ValueRange.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Pass/PassRegistry.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project

    if (op.getAdjX()) std::swap(equation[3], equation[4]);
    if (op.getAdjY()) std::swap(equation[6 + 3], equation[6 + 4]);

    rewriter.replaceOpWithNewOp<TF::EinsumOp>(
        op, op.getType(),
        /*inputs=*/ValueRange({input_lhs, input_rhs}),
        /*equation=*/equation);

    return success();
  }
};

#define GEN_PASS_DEF_BATCHMATMULTOEINSUMPASS
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.h.inc"

  auto attr = CustomOption(&builder, "");
  Value random_result =
      builder
          .create<TFL::CustomOp>(TypeRange(result_ty_vec),
                                 ValueRange(result_shape_tensor_vec),
                                 custom_code, attr)
          .getResult(0);
  Value tulple_result = builder.create<stablehlo::TupleOp>(random_result);

  // a TF op with result type tensor<*xf32> will have a bounded type after
  // fallback legalization.
  auto cast_value = [&](OpBuilder& builder, Type result_type, ValueRange inputs,
                        Location loc) -> Value {
    return builder.create<mlir::tensor::CastOp>(loc, result_type,
                                                inputs.front());
  };