OpT op, typename OpT::Adaptor adaptor,
      ConversionPatternRewriter &rewriter) const override {
    Type dtype = op.getElementDtype();
    if (!(dtype.isF16() || dtype.isF32() || dtype.isF64() ||
          dtype.isInteger(1) || dtype.isInteger(8) || dtype.isInteger(16) ||
          dtype.isInteger(32) || dtype.isInteger(64))) {
      const char *error_info =
          "requires element_dtype to be 1-bit/8-bit/16-bit/32-bit/64-bit "

  // ALWAYS-DECOMPOSE-NOT:  "tf.AssignAddVariableOp"
  "tf.AssignAddVariableOp"(%0, %1) {dtype = "tfdtype$DT_INT32"} : (tensor<*x!tf_type.resource<tensor<2x8xi32>>>, tensor<i32>) -> ()
  // CHECK: tf_device.cluster
  "tf_device.cluster"() ({
    // CHECK-NOT: "tf.AssignAddVariableOp"
    "tf.AssignAddVariableOp"(%0, %1) {dtype = "tfdtype$DT_INT32"} : (tensor<*x!tf_type.resource<tensor<2x8xi32>>>, tensor<i32>) -> ()
    tf_device.return

shared_name: the name by which this variable is referred to.
dtype and shape: attributes representing the data type and shape held in the
  variable.

Example:
    resource_variable_ops.var_handle_op(
          dtype=dtypes.int32, shape=[8, 16], container="foo", shared_name="bar")
  returns a handle for a variable with name "bar" in container "foo", and the
  variable holds a tensor of shape [8, 16] and dtype int32.
  }];

  let arguments = (ins

		for _, cType := range []batch.JobConditionType{batch.JobFailed, batch.JobComplete, batch.JobFailureTarget} {
			if IsConditionTrue(oldJob.Status.Conditions, cType) && !IsConditionTrue(job.Status.Conditions, cType) {
				allErrs = append(allErrs, field.Invalid(statusFld.Child("conditions"), field.OmitValueType{}, fmt.Sprintf("cannot disable the terminal %s=True condition", string(cType))))
			}
		}
	}

REGISTER_NO_GRADIENT_OP("Floor");

// Conjugate helper function returns the conjugate of an Output if it
// is complex valued.
Output ConjugateHelper(const Scope& scope, const Output& out) {
  DataType dtype = out.type();
  if (dtype == DT_COMPLEX64 || dtype == DT_COMPLEX128) {
    return Conj(scope, out);
  } else {
    return out;
  }
}

// TODO(andydavis) Add control dependencies to gradient functions (as needed).

  // CHECK: "tf.TensorArrayV3"

func ensureJobConditionStatus(list []batch.JobCondition, cType batch.JobConditionType, status v1.ConditionStatus, reason, message string, now time.Time) ([]batch.JobCondition, bool) {
	if condition := findConditionByType(list, cType); condition != nil {
		if condition.Status != status || condition.Reason != reason || condition.Message != message {
			*condition = *newCondition(cType, status, reason, message, now)
			return list, true
		}

    private void ambiguousClass(ClassNode type, ClassNode iType, String name) {
        if (type.getName().equals(iType.getName())) {
            addError("reference to " + name + " is ambiguous, both class " + type.getName() + " and " + iType.getName() + " match", type);
        } else {
            type.setRedirect(iType);
        }
    }

    private boolean resolveAliasFromModule(ClassNode type) {

    return false;
  }

  if (n->type_string() != "AssignAddVariableOp" &&
      n->type_string() != "AssignSubVariableOp") {
    return false;
  }

  DataType dtype;
  if (!TryGetNodeAttr(n->def(), "dtype", &dtype) || !DataTypeIsInteger(dtype)) {
    return false;
  }

  Node* const_input = nullptr;
  for (const Edge* e : n->in_edges()) {
    if (!e->IsControlEdge() && e->src()->IsConstant()) {

func testServeFileContentType(t *testing.T, mode testMode) {
	const ctype = "icecream/chocolate"
	ts := newClientServerTest(t, mode, HandlerFunc(func(w ResponseWriter, r *Request) {
		switch r.FormValue("override") {
		case "1":
			w.Header().Set("Content-Type", ctype)
		case "2":
			// Explicitly inhibit sniffing.
			w.Header()["Content-Type"] = []string{}
		}
		ServeFile(w, r, "testdata/file")
	})).ts