// TF-DAG: return %[[identity]]

// XLA-LABEL: func @conv_with_int_per_layer
// XLA-DAG: %[[w0_float:.*]] = "tf.Const"() <{value = dense<{{\[\[\[\[}}-0.282878935, -0.211567819
// XLA-DAG: %[[b0_float:.*]] = "tf.Const"() <{value = dense<[-0.0192535277, -5.998660e-03]> : tensor<2xf32>}> : () -> tensor<2xf32>
// XLA-DAG: %[[w1_float:.*]] = "tf.Const"() <{value = dense<{{\[\[\[\[}}0.208403707, 0.478067577

        "@local_xla//xla:status_macros",
        "@local_xla//xla/client:local_client",
        "@local_xla//xla/pjrt:pjrt_client",
        "@local_xla//xla/pjrt:pjrt_future",
        "@local_xla//xla/pjrt:pjrt_stream_executor_client",
        "@local_xla//xla/pjrt:tracked_device_buffer",
        "@local_xla//xla/service:shaped_buffer",
        "@local_xla//xla/stream_executor:device_memory_allocator",

        [],
        otherwise = [
            "@local_xla//xla/stream_executor/cuda:all_runtime",
            "@local_xla//xla/stream_executor/cuda:cuda_driver",
            "@local_xla//xla/stream_executor/cuda:cuda_platform",
            "@local_xla//xla/stream_executor/cuda:cudnn_plugin",
            "@local_xla//xla/stream_executor/cuda:cufft_plugin",
            "@local_xla//xla/stream_executor:cuda_platform",
        ],
    ),

#     --config=dbg --per_file_copt=+tensorflow/core/kernels/identity_op.*@-g
# Since this .bazelrc file is synced between the tensorflow/tensorflow repo and
# the openxla/xla repo, also include debug info for files under xla/.
build:dbg --per_file_copt=+.*,-tensorflow.*,-xla.*@-g0
build:dbg --per_file_copt=+tensorflow/core/kernels.*@-g0
# for now, disable arm_neon. see: https://github.com/tensorflow/tensorflow/issues/33360

#include "tensorflow/compiler/tf2xla/const_analysis.h"
#include "tensorflow/compiler/tf2xla/resource_operation_table.h"
#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
#include "xla/service/graphcycles/graphcycles.h"
#include "xla/statusor.h"
#include "xla/union_find.h"
#include "xla/util.h"
#include "tensorflow/core/common_runtime/function.h"
#include "tensorflow/core/common_runtime/graph_constructor.h"
#include "tensorflow/core/framework/bounds_check.h"

      llvm::cl::desc("Choose target opset."),
      llvm::cl::values(
          clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),
          clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",
                     "Uses TF Uniform Quantized ops"))};

  Option<bool> enable_per_channel_quantization_{

}

def TF_TPUPartitionedOutputOp : TF_Op<"TPUPartitionedOutput", [Pure]> {
  let summary = [{
An op that demultiplexes a tensor to be sharded by XLA to a list of partitioned
  }];

  let description = [{
outputs outside the XLA computation.
  }];

  let arguments = (ins
    TF_Tensor:$inputs,

    DefaultValuedOptionalAttr<I64Attr, "0">:$partition_dim,

        "@llvm-project//mlir:Support",
        "@llvm-project//mlir:TransformUtils",
        "@llvm-project//mlir:Transforms",
        "@local_xla//xla:status",
        "@local_xla//xla:statusor",
        "@local_xla//xla/mlir_hlo",
        "@stablehlo//:stablehlo_ops",
    ],
)

cc_library(
    name = "tensorflow_lite_optimize",
    srcs = [
        "transforms/generated_optimize.inc",
        "transforms/optimize.cc",

#include "tensorflow/compiler/jit/shape_inference_helpers.h"
#include "tensorflow/compiler/jit/xla_cluster_util.h"
#include "tensorflow/compiler/tf2xla/const_analysis.h"
#include "xla/service/graphcycles/graphcycles.h"
#include "xla/status_macros.h"
#include "tensorflow/core/common_runtime/device_factory.h"
#include "tensorflow/core/common_runtime/function.h"
#include "tensorflow/core/common_runtime/optimization_registry.h"

                ' of compute capabilities excluding version %s.' % ver)
            all_valid = False
          if ver < 35:
            print('WARNING: XLA does not support CUDA compute capabilities '
                  'lower than sm_35. Disable XLA when running on older GPUs.')
      else:
        ver = float(m.group(0))
        if ver < 3.0: