// ExtendSessionGraphHelper manually.
  std::atomic<bool> extend_before_run;
};

struct TF_ImportGraphDefOptions {
  tensorflow::ImportGraphDefOptions opts;

  // Backing memory for TensorId fields in opts.
  // TODO(skyewm): it'd be better if ImportGraphDefOptions owned this.
  std::vector<tensorflow::string> tensor_id_data;
};

struct TF_ImportGraphDefResults {

    // .backing_device of shape is CPU since the tensor is backed by CPU
    backing_device_name =
        TFE_TensorHandleBackingDeviceName(retvals[0], status.get());
    ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
    ASSERT_TRUE(absl::StrContains(backing_device_name, "CPU:0"))
        << backing_device_name;

    TFE_DeleteOp(shape_op);

      TF_DeleteTensor);
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  ASSERT_EQ(111., *static_cast<float*>(TF_TensorData(resolved_value.get())));

  // Free the backing buffer for the variable.
  op.reset(TFE_NewOp(context.get(), "DestroyResourceOp", status.get()));
  TFE_OpAddInput(op.get(), var_handle, status.get());
  TFE_OpSetDevice(op.get(), name, status.get());

you want to debug a kernel, you can compile specific files with `-g` using the
`--per_file_copt` bazel option. For example, if you want to debug the Identity
op, which are in files starting with `identity_op`, you can run

```bash
bazel build --config=dbg --per_file_copt=+tensorflow/core/kernels/identity_op.*@-g //tensorflow/tools/pip_package:build_pip_package
```

      // Destroying pruning_thread_ will block until Prune() receives the above
      // notification and returns.
      pruning_thread_.reset();
    }
  }

  /// Read `n` bytes from `filename` starting at `offset` into `buffer`. It
  /// returns total bytes read ( -1 in case of errors ). This method will set
  /// `status` to:
  ///
  /// 1) The error from the remote filesystem, if the read from the remote

    ASSERT_TRUE(remote_arg->HasLocalMirror(nullptr));
  }

  if (remote_func_outputs) {
    const string backing_device =
        TFE_TensorHandleBackingDeviceName(retvals[0], status);
    ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
    EXPECT_EQ(backing_device, task2_name);
  }

  auto* retval_task0 = TFE_TensorHandleCopyToDevice(

      to the Licensor or its representatives, including but not limited to
      communication on electronic mailing lists, source code control systems,
      and issue tracking systems that are managed by, or on behalf of, the
      Licensor for the purpose of discussing and improving the Work, but
      excluding communication that is conspicuously marked or otherwise

        functions.

## Deprecations

*   TensorFlow 1.7 may be the last time we support Cuda versions below 8.0.
    Starting with TensorFlow 1.8 release, 8.0 will be the minimum supported
    version.
*   TensorFlow 1.7 may be the last time we support cuDNN versions below 6.0.
    Starting with TensorFlow 1.8 release, 6.0 will be the minimum supported
    version.

## Thanks to our Contributors

  /// operations.
  ///
  /// This operation must be provided. See "REQUIRED OPERATIONS" above.
  void (*cleanup)(TF_RandomAccessFile* file);

  /// Reads up to `n` bytes from `*file` starting at `offset`.
  ///
  /// The output is in `buffer`, core TensorFlow owns the buffer and guarantees
  /// that at least `n` bytes are available.
  ///

    write_action_env_to_bazelrc('HIP_PLATFORM', environ_cp.get('HIP_PLATFORM'))

  if is_windows():
    print('\nWARNING: Cannot build with CUDA support on Windows.\n'
          'Starting in TF 2.11, CUDA build is not supported for Windows. '
          'For using TensorFlow GPU on Windows, you will need to build/install '
          'TensorFlow in WSL2.\n')
    environ_cp['TF_NEED_CUDA'] = '0'
  else: