}

// `DumpTensor` op saves entire value of input to as a tensor proto into a
// specified directory and filename. When enabled is set to false, op is
// disabled and won't save any value. It also creates `QuantizationUnit` proto
// with `func_name` and `node_name` to identify the op.
REGISTER_OP("DumpTensor")
    .Input("tensor_data: T")
    .Attr("log_dir_path: string")
    .Attr("file_name: string")
    .Attr("T: type")

    module_op.emitError() << "quant-convert-tpu-model-to-cpu pattern "
                             "conversion did not converge.";
    signalPassFailure();
    return;
  }
}

}  // namespace

// Creates an instance of `ConvertTpuModelToCpuPass`.
std::unique_ptr<OperationPass<ModuleOp>> CreateConvertTpuModelToCpuPass() {
  return std::make_unique<ConvertTpuModelToCpuPass>();
}

#include "tensorflow/compiler/jit/xla_compilation_cache.pb.h"
#include "xla/service/hlo.pb.h"
#include "tensorflow/core/platform/path.h"
#include "tensorflow/core/public/session.h"

namespace tensorflow {
namespace {

// Creates a float tensor of linearly increasing values, starting from offset.
Tensor CreateInputTensor(const TensorShape& shape, float offset) {
  Tensor tensor(DT_FLOAT, shape);
  for (int64 i = 0; i < tensor.flat<float>().size(); ++i) {

            ctx);
    if (failed(applyPatternsAndFoldGreedily(func_op, std::move(patterns_2)))) {
      signalPassFailure();
    }
  }
}

}  // namespace

// Creates an instance of the TensorFlow dialect PrepareQuantize pass.
std::unique_ptr<OperationPass<ModuleOp>> CreatePrepareQuantizePass(
    const bool enable_per_channel_quantized_weight, const int bit_width) {

    if (failed(ApplyQuantizationSpec(spec, module_op))) {
      signalPassFailure();
      return;
    }
  }
}

}  // namespace

// Creates `LiftQuantizableSpotsAsFunctionsPass` with user-defined
// `QuantizationSpecs`.
std::unique_ptr<OperationPass<ModuleOp>>
CreateLiftQuantizableSpotsAsFunctionsPass(
    const QuantizationSpecs& quantization_specs) {

  test::GetTensors(root, {add}, &out);
  test::ExpectTensorNear<float>(out[0], test::AsTensor<float>({42.0f}, {}),
                                1e-5);

  out.clear();
  // Note : GetTensors creates a new session, so 'v' is uninitialized.
  // sub should have no control deps, so it should not cause the assign to run.
  // Hence is_inited should be false.
  test::GetTensors(root, {sub, is_inited}, &out);

    new_operands.push_back(block->addArgument(t, loc));
  auto call = builder.create<func::CallOp>(loc, func, new_operands);
  builder.create<YieldOp>(loc, call.getResults());
  // Mark old function as private so that it can be DCE'd if not called.
  func.setPrivate();
}

void RunOnWhile(TF::WhileOp while_op) {
  Operation* op = while_op.getOperation();
  // Create new TFL While op that will be used to replace TF While op.

  if (failed(applyPatternsAndFoldGreedily(module_op, std::move(patterns)))) {
    module_op.emitError() << "quant-cast-bf16-ops-to-f32 failed.";
    signalPassFailure();
  }
}

}  // namespace

// Creates an instance of the Cast BF16 ops to F32 pass.
std::unique_ptr<OperationPass<ModuleOp>> CreateCastBf16OpsToF32Pass() {
  return std::make_unique<CastBf16OpsToF32Pass>();
}

                               "attribute in the quantized composite function.";
      signalPassFailure();
    }
  }
}

}  // namespace

// Creates an instance of the pass for inserting quantized functions.
std::unique_ptr<OperationPass<ModuleOp>> CreateInsertQuantizedFunctionsPass(
    QuantMethod quantization_method, OpSet target_opset) {

    return;
  }

  auto new_func_type =
      builder.getFunctionType(new_input_types, new_output_types);
  func.setType(new_func_type);
}
}  // namespace

// Creates an instance of the TensorFlow Lite modify io nodes pass.
std::unique_ptr<OperationPass<func::FuncOp>> CreateModifyIONodesPass(
    Type input_type, Type output_type) {