in taking those formats, modifying them, and/or converting them to intermediate
formats that can be processed by TensorFlow.

These modifications and conversions are handled by a variety of libraries that
have different security properties and provide different levels of confidence
when dealing with untrusted data. Based on the security history of these

        ],
    }),
)

cc_library(
    name = "immediate_execution_tensor_handle",
    srcs = ["immediate_execution_tensor_handle.cc"],
    hdrs = ["immediate_execution_tensor_handle.h"],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [
        ":abstract_tensor_handle",
        "//tensorflow/c:tensor_interface",
        "//tensorflow/core:framework",

  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_TensorHandle* var_handle = nullptr;
  int num_retvals = 1;
  TFE_Execute(op, &var_handle, &num_retvals, status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_DeleteOp(op);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  CHECK_EQ(1, num_retvals);
  TF_DeleteStatus(status);
  return var_handle;
}

TFE_TensorHandle* CreateVariable(TFE_Context* ctx, float value,

  tracing::TracingTensorHandle* handle = nullptr;
  for (auto input : inputs) {
    PartialTensorShape shape;
    TF_RETURN_IF_ERROR(input->Shape(&shape));
    TF_RETURN_IF_ERROR(dyn_cast<tracing::TracingContext>(ctx)->AddParameter(
        input->DataType(), shape, &handle));
    params->emplace_back(handle);
  }
  return absl::OkStatus();
}

*   Updates `libjpeg-turbo` to `2.0.5` to handle
    [CVE-2020-13790](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13790).
*   Updates `junit` to `4.13.1` to handle
    [CVE-2020-15250](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15250).
*   Updates `PCRE` to `8.44` to handle
    [CVE-2019-20838](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-20838)
    and

w/core/tfrt/mlrt/kernel,tensorflow/core/tfrt/mlrt/bytecode,tensorflow/core/tfrt/mlrt/interpreter,tensorflow/compiler/mlir/tfrt/translate/mlrt,tensorflow/compiler/mlir/tfrt/translate/mlrt/testdata,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils,tensorflow/core/tfrt/utils/debug,tensorflow/c...

limitations under the License.
==============================================================================*/
#include "tensorflow/c/experimental/gradients/math_grad.h"

#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/gradients.h"
#include "tensorflow/c/experimental/ops/array_ops.h"
#include "tensorflow/c/experimental/ops/math_ops.h"
#include "tensorflow/c/experimental/ops/nn_ops.h"

using std::vector;

                      ", dtype=", DataType_Name(DataType()),
                      ", type=", FullType().DebugString(), ")");
}

Status AbstractTensorHandle::TensorHandleStatus() const {
  // Tensor handles in current runtime don't carry error info and this method
  // should always return OK status.
  return absl::OkStatus();
}

    if (it != loaded_libs->end()) {
      lib_handle->lib_handle = it->second;
    } else {
      status->status =
          env->LoadDynamicLibrary(library_filename, &lib_handle->lib_handle);
      if (status->status.ok()) {
        TF_CHECK_OK(
            tensorflow::RegisterPluggableDevicePlugin(lib_handle->lib_handle));
      } else {
        delete lib_handle;
        return nullptr;
      }
    }

        "nn_grad.h",
    ],
    visibility = ["//visibility:private"],
    deps = [
        "//tensorflow/c/eager:abstract_tensor_handle",
        "//tensorflow/c/eager:gradients_internal",
        "//tensorflow/c/eager:immediate_execution_context",
        "//tensorflow/c/eager:immediate_execution_tensor_handle",
        "//tensorflow/c/experimental/ops:array_ops",
        "//tensorflow/c/experimental/ops:math_ops",