// will block till the operation that produces `h` has completed.
TF_CAPI_EXPORT extern const char* TFE_TensorHandleDeviceName(
    TFE_TensorHandle* h, TF_Status* status);

// Returns the name of the device in whose memory `h` resides.
//
// This function will block till the operation that produces `h` has completed.
TF_CAPI_EXPORT extern const char* TFE_TensorHandleBackingDeviceName(

```

### 3. Failure after conversion
If the conversion is successful, but the generated model is wrong, then state what is wrong:

- Model produces wrong results and/or has lesser accuracy.
- Model produces correct results, but it is slower than expected.

### 4. (optional) RNN conversion support
If converting TF RNN to TFLite fused RNN ops, please prefix [RNN] in the title.

```

## Requirements updater

Requirements updater is a standalone tool, intended to simplify process of
updating requirements for multiple minor versions of Python.

It takes in a file with a set of dependencies, and produces a more detailed
requirements file for each version, with hashes specified for each
dependency required, as well as their sub-dependencies.

### How to update/add requirements

}

// TF_Graph functions ---------------------------------------------------------

TF_Graph::TF_Graph()
    : graph(tensorflow::OpRegistry::Global()),
      refiner(graph.versions().producer(), graph.op_registry()),
      delete_requested(false),
      parent(nullptr),
      parent_inputs(nullptr) {
  // Tell the shape refiner to also run shape inference on functions.

// combined status.
TF_CAPI_EXPORT extern void TFE_ContextAsyncWait(TFE_Context* ctx,
                                                TF_Status* status);

// This function will block till the operation that produces `h` has
// completed. This is only valid on local TFE_TensorHandles. The pointer
// returned will be on the device in which the TFE_TensorHandle resides (so e.g.

  // eager tensors.
  void DeleteTrace(const AbstractTensorHandle*);

  // Consumes the internal state of the tape (so cannot be called more than
  // once unless the tape is persistent) and produces the gradient of the target
  // tensors with respect to the source tensors. The output gradients are used
  // if not empty and not null. The result is populated with one tensor per
  // target element.

  buf->Unref();
  return tensorflow::wrap(tensorflow::TensorHandle::CreateLocalHandle(
      std::move(t), device, device, context));
}

// This function will block till the operation that produces `h` has
// completed. This is only valid on local TFE_TensorHandles. Returns the size in
// bytes of the memory pointed to by the device pointer returned above.
size_t TFE_TensorHandleDeviceMemorySize(TFE_TensorHandle* h,

Yuan Tang, Yuki Ueda, Yusup, zhangshijin, zhuwenxi

# Release 2.0.1

## Bug Fixes and Other Changes

*   Fixes a security vulnerability where converting a Python string to a
    `tf.float16` value produces a segmentation fault
    ([CVE-2020-5215](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-5215))
*   Updates `curl` to `7.66.0` to handle

      const std::function<void(BackwardFunction*)>& backward_function_deleter);

  void DeleteTrace(int64_t tensor_id);

  // Consumes the internal state of the tape (so cannot be called more than
  // once) and produces the gradient of the target tensors with respect to the
  // source tensors. The output gradients are used if not empty and not
  // null. The result is populated with one tensor per target element.

                                                      TF_Status* status);

// In this code:
//   TF_Output producer = TF_OperationInput(consumer);
// There is an edge from producer.oper's output (given by
// producer.index) to consumer.oper's input (given by consumer.index).
TF_CAPI_EXPORT extern TF_Output TF_OperationInput(TF_Input oper_in);