// separate static method which returns a status.
  Variable(TFE_TensorHandle* handle, TF_DataType type)
      : handle_(handle), type_(type) {}

  // Helper for constructing a resource handle and wrapping it in a `Variable`
  // object.
  static Variable* Create(TFE_Context* context, TF_DataType type,
                          const int64_t* dims, const int num_dims,

operator in the script should be in the "math" category, and it will be
generated in the output file `c/experimental/ops/math_ops.cc`.

Running this script should be a no-op, generating identical code other than

  EXPECT_EQ(0, TF_OperationNumControlOutputs(neg));
  EXPECT_EQ(0, TF_OperationGetControlOutputs(neg, control_ops, 100));

  // Import it again, with an input mapping, return outputs, and a return
  // operation, into the same graph.
  TF_DeleteImportGraphDefOptions(opts);
  opts = TF_NewImportGraphDefOptions();
  TF_ImportGraphDefOptionsSetPrefix(opts, "imported2");

      // Run shape inference function for newly added node.
      status->status = desc->graph->refiner.AddNode(ret);
    }
    if (status->status.ok()) {
      // Add the node to the name-to-node mapping.
      desc->graph->name_map[ret->name()] = ret;
    } else if (ret != nullptr) {
      desc->graph->graph.RemoveNode(ret);
      ret = nullptr;
    }
  }

  delete desc;

// and weren't used as input to any node in the imported graph def. The number
// of fetched mappings is returned in `num_missing_unused_input_mappings`. The
// array of each mapping's source node name is returned in `src_names`, and the
// array of each mapping's source index is returned in `src_indexes`.
//
// `*src_names`, `*src_indexes`, and the memory backing each string in
// `src_names` are owned by and have the lifetime of `results`.

# hope to provide a better mechanism for control over symbol exporting, and
# then tackle this issue again.
#
# TODO: Remove the following two lines once TF doesn't depend on Bazel wrapping
# all library archives in -whole_archive -no_whole_archive.
build --noincompatible_remove_legacy_whole_archive
build --features=-force_no_whole_archive

          echo "success."
        }
        # See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/toolchains/remote_config/configs.bzl
        # This is a mapping of name_container_map keys under sigbuild_tf_configs
        # to tag names on gcr.io/tensorflow-sigs/build.
        # TF 2.9
        map sigbuild-r2.9 2.9-python3.9
        map sigbuild-r2.9-python3.8 2.9-python3.8

```
(You can paste links or attach files by dragging & dropping them below)
- Provide links to your updated versions of the above two colab notebooks.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```

#### Option B: Paste your code here or provide a link to a custom end-to-end colab

```
(You can paste links or attach files by dragging & dropping them below)

// inspect values. This would let people e.g. copy over most attributes and then
// modify some based on their values.

// A reference to an op's name -> attribute mapping
typedef struct TFE_OpAttrs TFE_OpAttrs;

// Fetch a reference to `op`'s attributes. The returned reference is only valid
// while `op` is alive.
TF_CAPI_EXPORT extern const TFE_OpAttrs* TFE_OpGetAttrs(const TFE_Op* op);

    relative imports in API `__init__.py` files.
*   Auto Mixed-Precision graph optimizer simplifies converting models to
    `float16` for acceleration on Volta and Turing Tensor Cores. This feature
    can be enabled by wrapping an optimizer class with
    `tf.train.experimental.enable_mixed_precision_graph_rewrite()`.
*   Add environment variable `TF_CUDNN_DETERMINISTIC`. Setting to "true" or "1"