// Example:
  //
  // cluster { job { name: "worker"
  //                 tasks { key: 0 value: "localhost:14522" }
  //                 tasks { key: 1 value: "localhost:14523" }
  //               }
  //           job { name: "client"
  //                 tasks { key: 0 value: "localhost:14524" }
  //               }
  //         } job_name: "worker" protocol: "grpc"
  //

# Move the updated file to the appropriate directory
mv "$REQUIREMENTS_LOCK_FILE" ../wheel_test/

#include "tensorflow/core/framework/device_attributes.pb.h"
#include "tensorflow/core/framework/op_def.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/casts.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/util/managed_stack_trace.h"

struct TFE_Op;

namespace tensorflow {

class ImmediateExecutionContext;
class AbstractOpAttrs;

                                                    const char* key,
                                                    TF_Status* status);

// Report error (specified by error_code and error_message) to other tasks in
// the cluster.
TF_CAPI_EXPORT extern void TFE_ReportErrorToCluster(TFE_Context* ctx,
                                                    int error_code,

        ],
        "//conditions:default": [
            "//tensorflow/core:framework",
            "//tensorflow/core:lib",
            "//tensorflow/core:protos_all_cc",
            "//tensorflow/core/platform:casts",
        ],
    }),
)

tf_cuda_library(
    name = "tf_tensor_internal",
    hdrs = [
        "tf_tensor.h",
        "tf_tensor_helper.h",
        "tf_tensor_internal.h",
    ],

        "Coordination service is not enabled.");
    return;
  }
  std::vector<tensorflow::CoordinatedTask> task_vec(tasks.length);
  auto* task_iter = static_cast<const tensorflow::CoordinatedTask*>(tasks.data);
  for (size_t i = 0; i < tasks.length; ++i) {
    task_vec[i].set_job_name(task_iter->job_name());
    task_vec[i].set_task_id(task_iter->task_id());
    ++task_iter;
  }

#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/rendezvous.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/platform/casts.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/platform.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/profiler/lib/traceme.h"

    "tf_copts",
    "tf_cuda_cc_test",
    "tf_cuda_library",
)
load("//tensorflow:tensorflow.default.bzl", "cc_header_only_library", "filegroup")
load(
    "//tensorflow/core/platform:build_config_root.bzl",
    "tf_cuda_tests_tags",
)
load("//tensorflow/core/platform:rules_cc.bzl", "cc_library")

package(
    # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"],
    licenses = ["notice"],
)

tf_cuda_library(

        This class implements the sharpness-aware minimization technique, which
        boosts model performance on various tasks, e.g., ResNet on image
        classification.

*   `tf.data`:

    *   Added support for cross-trainer data caching in tf.data service. This

#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
#include "tensorflow/core/common_runtime/eager/eager_operation.h"
#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h"
#include "tensorflow/core/platform/casts.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/protobuf/cluster.pb.h"
#include "tensorflow/core/protobuf/tensorflow_server.pb.h"

namespace {