# Check the converted model in the XLA opset.
    quantization_options = quant_opts_pb2.QuantizationOptions(
        quantization_method=quant_opts_pb2.QuantizationMethod(
            preset_method=_PresetMethod.METHOD_STATIC_RANGE_INT8
        ),
        tags=tags,
        signature_keys=[signature_key],
        op_set=quant_opts_pb2.XLA,
        enable_two_input_tensors=not use_kernel,
    )

#include "tensorflow/compiler/jit/encapsulate_util.h"
#include "tensorflow/compiler/tf2xla/side_effect_util.h"
#include "tensorflow/compiler/tf2xla/tf2xla_util.h"
#include "xla/status_macros.h"
#include "xla/xla_data.pb.h"
#include "tensorflow/core/common_runtime/function.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/graph_to_functiondef.h"

bool ShapeInference::InferShapeForXlaGatherOp(XlaGatherOp op) {
  xla::Shape input_shape = xla::TypeToShape(op.getOperand().getType());
  if (input_shape == xla::Shape() || input_shape.is_unbounded_dynamic())
    return false;

  xla::Shape start_indices_shape =
      xla::TypeToShape(op.getStartIndices().getType());
  if (start_indices_shape == xla::Shape()) return false;

  xla::GatherDimensionNumbers gather_dim_numbers;

}

def TPUValidateInputsPass : Pass<"tf-tpu-validate-inputs", "ModuleOp"> {
  let summary = "Validates inputs to the TPU TF/XLA bridge";

  let description = [{
    This pass checks that the IR has valid input to TPU TF/XLA bridge.
    It checks the relations of multiple ops. Properties of single ops are
    checked by the 'verify' method of ops.
  }];

      tf_device.return %2 : tensor<2xi32>
    }

    func.return %1 : tensor<2xi32>
  }

  // Check that a non-XLA value is not routed through the XLA side.

  // CHECK-LABEL: func @nonxla_static
  func.func @nonxla_static() -> () {
    "tf_device.cluster"() ({
      %0 = "tf.A"() : () -> (tensor<i32>)

  func.func @unparsable_element_input_sharding_configuration(%arg0: tensor<?xi32>) {
    // expected-error@+1 {{bad 'input_sharding_configuration' attribute at index 0 with value 'test': failed to parse to xla::OpSharding}}

#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/passes.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_dialect.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"
#include "tensorflow/core/framework/kernel_shape_util.h"
#include "tensorflow/core/lib/math/math_util.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/util/padding.h"

        DenseIntElementsAttr::get(output_int_type, output_ty.getShape());

    // use int32_t instead of int64_t if all elements are in the range of int32
    // because int64 is not supported in dynamic reshape in XLA
    bool elements_all_in_int32_range =
        std::all_of(output_ty.getShape().begin(), output_ty.getShape().end(),
                    IsWithinInt32Range);

    if (elements_all_in_int32_range) {

  let summary = "DynamicUpdateSlice.";
  let description = [{
    DynamicUpdateSlice op that have the same semantics with XLA
    DynamicUpdateSlice.
    Generates a result which is the value of the input array
    operand, with a slice update overwritten at start_indices.

    See https://www.tensorflow.org/xla/operation_semantics#dynamicupdateslice.
  }];

  let arguments = (ins

#include "tensorflow/compiler/mlir/tensorflow/utils/error_util.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/mangling_util.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/translate_utils.h"
#include "xla/status_macros.h"
#include "tensorflow/core/common_runtime/function.h"
#include "tensorflow/core/common_runtime/graph_constructor.h"
#include "tensorflow/core/common_runtime/shape_refiner.h"