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>

  // Inserts a placeholder node in the graph to replace a feed output tensor,
  // and returns the new placeholder node and a boolean indicating if the
  // original input node was removed from the graph. Uses of the feed output
  // tensor are replaced with this placeholder node. If the feed output tensor
  // is of a single output node, the control dependencies are forwarded to the

    (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

// leaving behind non-GC'ed state.
//
// Detailed motivation behind this approach, from ashankar@:
//
// Each call to Session::Run() that identifies a new subgraph (based on feeds
// and fetches) creates some datastructures that live as long as the session
// (the partitioned graph, associated executors etc.).
//
// A pathological case of this would be if say the initialization op

                              encapsulating_function, uncompilable_nodes);
    LogNotCompilable(node, uncompilable_reason);
    return false;
  }

  // _Arg nodes in a top-level function represent feeds and _Retval nodes in a
  // top-level function represent fetches.
  if (stack_depth == 1 &&
      (node.type_string() == "_Arg" || node.type_string() == "_Retval")) {

         llvm::zip(yield->getOperands().drop_front(1), block.getArguments())) {
      if (yield_operand != block_arg) return std::nullopt;
    }
  } else {
    // All call results should feed into expected consumer
    // All results of the call should feed into the yield.
    if (call.getNumResults() != call_consumer->getNumOperands())
      return std::nullopt;
  }

// the in-loop adjustment is 5 (for example), then there is not much
// point treating it as inlinable. On the other hand "bar" has a param
// property (parameter "x" feeds unmodified to an "if" statement") and
// a return property (always returns same constant) meaning that a
// given call _could_ be rescored down as much as -35 points-- thus if