* Range#contains(Comparable) range.contains(k)}, then {@link #get(Comparable) get(k)} will return
   * {@code value}.
   *
   * <p>If {@code range} {@linkplain Range#isEmpty() is empty}, then this is a no-op.
   */
  void put(Range<K> range, V value);

  /**
   * Maps a range to a specified value, coalescing this range with any existing ranges with the same

    (by default) transferred to the TensorFlow team. This means that the benefit
    of the contribution must be compared against the cost of maintaining the
    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

        @Override
        public Subject getSubject() {
            return null; // not relevant for renew() behavior
        }

        @Override
        public void refresh() throws CIFSException {
            // no-op for tests
        }

        @Override
        public boolean isGuest() {
            return false; // not guest for test purposes
        }

        @Override
        public boolean isAnonymous() {

		writeErrorResponseJSON(ctx, w, toAdminAPIErr(ctx, err), r.URL)
		return
	}
	vars := mux.Vars(r)
	op := madmin.SiteResyncOp(vars["operation"])
	var (
		status madmin.SRResyncOpStatus
		err    error
	)
	switch op {
	case madmin.SiteResyncStart:
		status, err = globalSiteReplicationSys.startResync(ctx, objectAPI, peerSite)
	case madmin.SiteResyncCancel:

        for (SMBMessage op : pendingOps) {
            try {
                ChannelInfo alternativeChannel = manager.selectChannel(op);
                alternativeChannel.getTransport().send(op);
            } catch (Exception e) {
                log.error("Failed to redistribute operation", e);
                // Notify waiting threads of failure
                op.notifyError(e);
            }
        }

TF_Operation* Mul(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
                  TF_Status* s, const char* name = "mul");

// If `op_device` is non-empty, set the created op on that device.
TF_Operation* MinWithDevice(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
                            const string& op_device, TF_Status* s,
                            const char* name = "min");

        final String password = form.password;
        form.clearSecurityInfo();
        try {
            final HtmlResponse loginRedirect = fessLoginAssist.loginRedirect(new LocalUserCredential(username, password), op -> {}, () -> {
                activityHelper.login(getUserBean());
                userInfoHelper.deleteUserCodeFromCookie(request);
                return getHtmlResponse();
            });

                            /*use_function=*/!std::get<2>(GetParam()));
  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
  ASSERT_EQ(
      "Provided null gradient_function for 'Neg'.\nIf the intent is to treat "
      "this op as non-differentiable consider using RegisterNotDifferentiable "
      "or NotDifferentiableGradientFunction.",
      s.message());
  ASSERT_EQ(nullptr, outputs[0]);
}

        }
        saveInfo(messages -> messages.addSuccessBulkProcessStarted(GLOBAL));
        return redirect(getClass()); // no-op
    }

    /**
     * Import the file asynchronously.
     * @param fileName The file name.
     * @param tempFile The temporary file.
     */

# ops. Since other languages use the same libtensorflow_framework.so, op
# libraries are language agnostic.
#
# This shared object is not used unless framework_shared_object=true (set in the
# configure script unconditionally); otherwise if it is false or undefined, the
# build is static and TensorFlow symbols (in Python only) are loaded into the
# global symbol table in order to support op registration. This means that