* Add ops to `tensorflow.raw_ops` that were missing.

* `tf.CheckpointOptions`

	// md5sum of the data to written is required as input for PutObject.

	md5hex := getMD5Hash(textData)
	sha256hex := ""

	// benchmark utility which helps obtain number of allocations and bytes allocated per ops.
	b.ReportAllocs()
	// the actual benchmark for PutObject starts here. Reset the benchmark timer.

	for i := 0; b.Loop(); i++ {
		// insert the object.

                                                    const char* name);

// Enables tracing of RunMetadata on the ops executed from this context.
TF_CAPI_EXPORT extern void TFE_ContextEnableRunMetadata(TFE_Context* ctx);

// Disables tracing of RunMetadata on the ops executed from this context.
TF_CAPI_EXPORT extern void TFE_ContextDisableRunMetadata(TFE_Context* ctx);

		adminRouter.Methods(http.MethodPut).Path(adminVersion+"/set-user-status").HandlerFunc(adminMiddleware(adminAPI.SetUserStatus)).Queries("accessKey", "{accessKey:.*}").Queries("status", "{status:.*}")

		// Service accounts ops
		adminRouter.Methods(http.MethodPut).Path(adminVersion + "/add-service-account").HandlerFunc(adminMiddleware(adminAPI.AddServiceAccount))

		maxCnt := 0
		signatureMap := map[uint64]int{}
		for _, v := range list {
			signatureMap[v]++
		}
		for ops, count := range signatureMap {
			if maxCnt < count && commonCount < ops {
				maxCnt = count
				commonCount = ops
			}
		}
		if maxCnt < readQuorum {
			return 0
		}
		return commonCount
	}

	commonWrite = filter(writes)

---
name: TensorFlow Lite Op Request
about: Use this template for reporting Lite ops you are using or missing
labels: 'comp:lite'

---

**System information**
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installed from (source or binary):
- TensorFlow version (or github SHA if from source):


**Provide the text output from tflite_convert**

```
# Copy and paste here
```

  // TF eager runtime.
  // TODO(b/172877902): These helper functions are currently used to support
  // PyFuncOp on TFRT, and might be useful for ops that directly use low
  // level TF APIs. Remove/replace the following functions when TFRT native
  // ops are implemented.
  //===--------------------------------------------------------------------===//
  // Create an abstract tensor handle from tensorflow::Tensor.

  bool async = false;
  TFE_ContextDevicePlacementPolicy device_placement_policy{
      TFE_DEVICE_PLACEMENT_SILENT};
  // If true, use TFRT backend
  bool use_tfrt = false;
  // Whether to run elementary eager ops wrapped in a call op.
  bool run_eager_op_as_function = false;
  // Whether to rewrite jit_compile functions.
  bool jit_compile_rewrite = false;
};

					}
				case testReadFrom:
					f := &testFile{ops: tf.ops}
					got, err := tw.readFrom(f)
					if _, ok := err.(testError); ok {
						t.Errorf("test %d, ReadFrom(): %v", i, err)
					} else if got != tf.wantCnt || !equalError(err, tf.wantErr) {
						t.Errorf("test %d, ReadFrom() = (%d, %v), want (%d, %v)", i, got, err, tf.wantCnt, tf.wantErr)
					}
					if len(f.ops) > 0 {

- 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.

### 5. (optional) Any other info / logs