---
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_CAPI_EXPORT extern void TFE_DeleteOp(TFE_Op* op);

// Returns the op or function name `op` will execute.
//
// The returned string remains valid throughout the lifetime of 'op'.
TF_CAPI_EXPORT extern const char* TFE_OpGetName(const TFE_Op* op,
                                                TF_Status* status);
TF_CAPI_EXPORT extern TFE_Context* TFE_OpGetContext(const TFE_Op* op,

	"github.com/zeebo/xxh3"
)

// Op is operation type.
//
//go:generate msgp -unexported -file=$GOFILE
//go:generate stringer -type=Op -output=msg_string.go -trimprefix=Op $GOFILE

// Op is operation type messages.
type Op uint8

// HandlerID is the ID for the handler of a specific type.
type HandlerID uint8

const (
	// OpConnect is a connect request.
	OpConnect Op = iota + 1

		ACB_DONT_REQUIRE_PREAUTH   = 0x00010000  /* 1 = Preauth not required */
	} SamrAcctFlags;

	[op(0x01)]
	int SamrCloseHandle([in] policy_handle *handle);

	[op(0x39)]
	int SamrConnect2([in,string,unique] wchar_t *system_name,
			[in] uint32_t access_mask,
			[out] policy_handle *handle);

	[op(0x3e)]
	int SamrConnect4([in,string,unique] wchar_t *system_name,
			[in] uint32_t unknown,

// Return an add op multiplying `a` by `b`.
TFE_Op* AddOp(TFE_Context* ctx, TFE_TensorHandle* a, TFE_TensorHandle* b);

// Return a matmul op multiplying `a` by `b`.
TFE_Op* MatMulOp(TFE_Context* ctx, TFE_TensorHandle* a, TFE_TensorHandle* b);

// Return an identity op.
TFE_Op* IdentityOp(TFE_Context* ctx, TFE_TensorHandle* a);

// Return a shape op fetching the shape of `a`.

// Infer shapes for the given `op`. The arguments mimic the arguments of the
// `shape_inference::InferenceContext` constructor. Note the following:
//   - The inputs of the `op` are not used for shape inference. So, it is
//     OK to not have the inputs properly set in `op`. See `input_tensors`
//     if you want shape inference to consider the input tensors of the
//     op for shape inference.

      // the operation would be a no-op, so exceptions are allowed but not required
    }
    expectUnchanged();
  }

  @MapFeature.Require(absent = SUPPORTS_PUT)
  public void testReplaceEntry_unsupportedAbsentKey() {
    try {
      getMap().replace(k3(), v3(), v4());
    } catch (UnsupportedOperationException tolerated) {
      // the operation would be a no-op, so exceptions are allowed but not required
    }

      // the operation would be a no-op, so exceptions are allowed but not required
    }
    expectUnchanged();
  }

  @MapFeature.Require(absent = SUPPORTS_PUT)
  public void testReplaceEntry_unsupportedAbsentKey() {
    try {
      getMap().replace(k3(), v3(), v4());
    } catch (UnsupportedOperationException tolerated) {
      // the operation would be a no-op, so exceptions are allowed but not required
    }

     */
    @Execute
    @Secured({ ROLE, ROLE + VIEW })
    public HtmlResponse search(final SearchForm form) {
        copyBeanToBean(form, duplicateHostPager, op -> op.exclude(Constants.PAGER_CONVERSION_RULE));
        return asHtml(path_AdminDuplicatehost_AdminDuplicatehostJsp).renderWith(data -> {
            searchPaging(data, form);
        });
    }

    /**

     * @return HTML response for the search results
     */
    @Execute
    @Secured({ ROLE, ROLE + VIEW })
    public HtmlResponse search(final SearchForm form) {
        copyBeanToBean(form, keyMatchPager, op -> op.exclude(Constants.PAGER_CONVERSION_RULE));
        return asHtml(path_AdminKeymatch_AdminKeymatchJsp).renderWith(data -> {
            searchPaging(data, form);
        });
    }

    /**