)

cc_library(
    name = "grappler_hdrs",
    hdrs = ["grappler.h"],
    visibility = ["//tensorflow:internal"],
    deps = [
        "//tensorflow/c:c_api",
        "//tensorflow/c:c_api_macros",
        "//tensorflow/c:tf_status_headers",
    ],
)

cc_library(
    name = "grappler",
    srcs = ["grappler.cc"],
    hdrs = [
        "grappler.h",
        "grappler_internal.h",
    ],

#include "tensorflow/core/common_runtime/device_factory.h"
#include "tensorflow/core/common_runtime/device_mgr.h"
#include "tensorflow/core/grappler/clusters/virtual_cluster.h"
#include "tensorflow/core/grappler/grappler_item.h"
#include "tensorflow/core/grappler/grappler_item_builder.h"
#include "tensorflow/core/grappler/optimizers/meta_optimizer.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"

namespace tensorflow {
namespace {

        "//tensorflow/core/grappler:grappler_item",
        "//tensorflow/core/grappler:grappler_item_builder",
        "//tensorflow/core/grappler/clusters:virtual_cluster",
        "//tensorflow/core/grappler/optimizers:meta_optimizer",
        "//tensorflow/core/protobuf:for_core_protos_cc",
        "@llvm-project//llvm:Support",
    ],

    }
    for (const auto& input : specs.inputs) {
      terminal_nodes.push_back(input.first);
    }
    TF_RETURN_IF_ERROR(tensorflow::grappler::SetTransitiveFaninGraph(
        graphdef, &pruned_graph_def, terminal_nodes));
    // TODO(ashwinm): Add a separate utility in grappler utils that abstracts
    // both SetTransitiveFaninGraph and restoring the missing contents from the

        "//tensorflow/core/grappler/clusters:cluster",
        "//tensorflow/core/grappler/clusters:single_machine",
        "//tensorflow/core/grappler/clusters:virtual_cluster",
        "//tensorflow/core/grappler/costs:graph_memory",
        "//tensorflow/core/grappler/graph_analyzer:graph_analyzer_tool",
        "//tensorflow/core/grappler/optimizers:meta_optimizer",

struct TfrtFunctionCompileOptions : public TfrtCompileOptions {
  // Currently only SavedModel API inference uses the tpu_fuse_ops option
  TfrtFunctionCompileOptions() {
    tpu_fuse_ops = false;
    // Currently grappler is not correctly applied in the eager execution of TF
    // functions, as it may sometimes remove arguments and results.
    enable_grappler = false;
  }

// Note: This implements the fusions performed in the old Remapper Grappler
// pass. That pass has specific cases for GPU and based on different
// target configurations on both CPU and GPU (Intel MKL, ROCm, etc.). This MLIR
// pass covers (some of) the general CPU case and at the moment does not account
// for any target-specific configurations.

// This pass is being ported over from the Grappler Remapper pass based on

ntegration_testing/... -//tensorflow/compiler/tf2tensorrt/... -//tensorflow/core/tpu/... -//tensorflow/lite/... -//tensorflow/tools/toolchains/...  -//tensorflow/go/... -//tensorflow/java/... -//tensorflow/core/grappler/optimizers:auto_mixed_precision_test_cpu -//tensorflow/core/grappler/optimizers:remapper_test_cpu -//tensorflow/core/kernels/image:resize_bicubic_op_test -//tensorflow/compiler/mlir/tfr/examples/customization:test_ops_test -//tensorflow/compiler/mlir/tfr/examples/mnist:mnist_ops_test...

        "//tensorflow/cc/saved_model:loader.h",
    ],
)

filegroup(
    name = "pywrap_required_hdrs",
    srcs = [
        "training/coordinator.h",
    ],
    visibility = ["//tensorflow/python/grappler:__pkg__"],
)

cc_library(
    name = "gradients",
    srcs = [
        "framework/gradients.cc",
        "framework/while_gradients.cc",
        "framework/while_gradients.h",
    ],

        summaries will be written, even when `tf.summary.record_if()` is not n
        effect, by returning True tensor if default writer is present.
*   Grappler:
    *   Disable default Grappler optimization timeout to make the optimization
        pipeline deterministic. This may lead to increased model loading time,
        because time spent in graph optimizations is now unbounded (was 20