* omitted} or {@linkplain #withPadChar(char) replaced}.
   *
   * <p>No line feeds are added by default, as per <a
   * href="http://tools.ietf.org/html/rfc4648#section-3.1">RFC 4648 section 3.1</a>, Line Feeds in
   * Encoded Data. Line feeds may be added using {@link #withSeparator(String, int)}.
   */
  public static BaseEncoding base64() {
    return BASE64;
  }

   * omitted} or {@linkplain #withPadChar(char) replaced}.
   *
   * <p>No line feeds are added by default, as per <a
   * href="http://tools.ietf.org/html/rfc4648#section-3.1">RFC 4648 section 3.1</a>, Line Feeds in
   * Encoded Data. Line feeds may be added using {@link #withSeparator(String, int)}.
   */
  public static BaseEncoding base64() {
    return BASE64;
  }

able to grab the feed and see the feed's actual URL in
the link rel="self", but the default value for that drops
the :port from the URL, and I cannot for the life of me
figure out how to get the Atom generator deep inside
django not to do that, or even where it is doing that,
or even what code is running to generate the Atom feed.
(I thought I knew but I added some assert False statements

  // inference.
  pm.addPass(mlir::TF::CreateGuaranteeAllFuncsOneUsePass());
  pm.addPass(mlir::TF::CreateTFShapeInferencePass());
  // For V1 compatibility, we process a module where the graph does not have
  // feeds and fetched. We extract first the TPU computation in a submodule,
  // where it'll be in a function with args and returned values, much more
  // like a TF v2 module. We can then run the usual pipeline on this nested

}

// Returns a TPUPartitionedInput op connected to a `tf_device.cluster_func`
// operand value if it has an XLA sharding. If value is a resource type then
// TPUPartitionedInput op will be connected to a ReadVariable op that feeds into
// a `tf_device.cluster_func`.
mlir::Operation* GetXlaShardingFromOperand(Value value) {
  Value value_to_visit = value;
  if (auto read_var = value_to_visit.getDefiningOp<mlir::TF::ReadVariableOp>())

      related config.
    representative_dataset: a generator that returns a dictionary in {input_key:
      input_value} format or a tuple with signature key and a dictionary in
      {input_key: input_value} format that feeds calibration data for quantizing
      model. This should be provided when the model is not a QAT model.

  Returns:
    A SavedModel object with TF quantization applied.

  Raises:

// constant arguments? Even constant arguments get an _Arg node in the graph
// instantiated for Function compilation. The tf2xla kernel for constant _Arg
// nodes takes the constant value, converts it to XlaLiteral, and feeds it
// to xla::ComputationBuilder.ConstantLiteral, which returns the handle. This
// constant XlaLiteral is included in the HLO graph, and subsequently, in
// the actual executable, which is copied to the device before being

     * Adapt a {@link MavenExecutionRequest} to a {@link Settings} object for use in the Maven core.
     * We want to make sure that what is ask for in the execution request overrides what is in the settings.
     * The CLI feeds into an execution request so if a particular value is present in the execution request
     * then we will take that over the value coming from the user settings.
     */

  // Ops can return multiple outputs and a control token for control
  // dependencies.
  // We don’t mention the control token in the return type here, it is implicit.
  %0, %ctl0 = tf_executor.opA %feed#0, %feed#1 : tensor<*xf32>
  %1, %ctl1 = tf_executor.opB : tensor<*xf32>
  %2, %ctl2 = tf_executor.opC %1, %ctl0 : tensor<*xf32>
  %3, %ctl3 = tf_executor.opD %2 : tensor<*xf32>
  tf_executor.fetch %3 : tensor<*xf32>

    (like `tf.Add`) they must be wrapped in the `tf_executor.island` operation.

    The `tf_executor.graph` operation does not accept any operands, inputs are
    implicitly captured by the region, representing the feeds to the graph.

    The region attached to `tf_executor.graph` is terminated by a
    `tf_executor.fetch` operation. The operands of the terminator correspond to