// on the given device.
TFE_TensorHandle* TestVariable(TFE_Context* ctx, float value,
                               const tensorflow::string& device_name = "");

// 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.

default margin-bottom\n  @include font-size($input-font-size); // Match inputs\n  font-weight: $font-weight-normal;\n  line-height: $input-line-height;\n  color: $input-group-addon-color;\n  text-align: center;\n  white-space: nowrap;\n  background-color: $input-group-addon-bg;\n  border: $input-border-width solid $input-group-addon-border-color;\n  @include border-radius($input-border-radius);\n\n  // Nuke default margins from checkboxes and radios to vertically center within.\n  input[type=\"radio\"],\n...

    feature.
*   Full new features (e.g., a new op implementing a cutting-edge algorithm)
    typically will live in
    [tensorflow/addons](https://github.com/tensorflow/addons) to get some
    airtime before a decision is made regarding whether they are to be migrated
    to the core.
*   As every PR requires several CPU/GPU hours of CI testing, we discourage

The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
  * https://github.com/tensorflow/addons
  * https://github.com/tensorflow/io (for I/O related ops)
If you depend on functionality not listed there, please file an issue.
"""
contrib = _LazyLoader("contrib", globals(), "tensorflow.contrib",

  TFE_DeleteOp(op);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  CHECK_EQ(0, num_retvals);

  TF_DeleteStatus(status);

  return var_handle;
}

TFE_Op* AddOp(TFE_Context* ctx, TFE_TensorHandle* a, TFE_TensorHandle* b) {
  TF_Status* status = TF_NewStatus();

  TFE_Op* op = TFE_NewOp(ctx, "AddV2", status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  if (gradient_tensors.size() == 1) {
    return gradient_tensors[0];
  }

  AbstractOperationPtr op(ctx_->CreateOperation());
  absl::Status s = op->Reset("AddN", /*raw_device_name=*/nullptr);
  if (!s.ok()) {
    return nullptr;
  }
  s = op->AddInputList(gradient_tensors);
  if (!s.ok()) {
    return nullptr;
  }

  int num_outputs = 1;

				"application/vnd.mozilla.xul+xml",
				"application/vnd.ms-artgalry",
				"application/vnd.ms-asf",
				"application/vnd.ms-cab-compressed",
				"application/vnd.ms-excel",
				"application/vnd.ms-excel.addin.macroenabled.12",
				"application/vnd.ms-excel.sheet.macroenabled.12",
				"application/vnd.ms-excel.sheet.binary.macroenabled.12",
				"application/vnd.ms-fontobject",
				"application/vnd.ms-htmlhelp",

		loi.NextMarker = opts.encodeMarker(last.Name)
		loi.NextVersionIDMarker = last.VersionID
	}
	return loi, nil
}

func maxKeysPlusOne(maxKeys int, addOne bool) int {
	if maxKeys < 0 || maxKeys > maxObjectList {
		maxKeys = maxObjectList
	}
	if addOne {
		maxKeys++
	}
	return maxKeys
}

  // Store pointer to raw buffer for validation of forwarding behaviour.
  TF_Tensor* orig = TFE_TensorHandleResolve(n, status);
  void* orig_ptr = TF_TensorData(orig);
  TF_DeleteTensor(orig);

  TFE_Op* add_op = AddOp(ctx, n, m);
  std::string cpu_device_name;
  ASSERT_TRUE(GetDeviceName(ctx, &cpu_device_name, "CPU"));
  TFE_OpSetDevice(add_op, cpu_device_name.c_str(), status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Test TF_Operation*() query functions.
  EXPECT_EQ(std::string("add"), std::string(TF_OperationName(add)));
  EXPECT_EQ(std::string("AddN"), std::string(TF_OperationOpType(add)));
  EXPECT_EQ(std::string(""), std::string(TF_OperationDevice(add)));
  EXPECT_EQ(1, TF_OperationNumOutputs(add));
  EXPECT_EQ(TF_INT32, TF_OperationOutputType(TF_Output{add, 0}));