if n > maxObletBytes {
		// Large object. Break into oblets for better
		// parallelism and lower latency.
		if b == s.base() {
			// Enqueue the other oblets to scan later.
			// Some oblets may be in b's scalar tail, but
			// these will be marked as "no more pointers",
			// so we'll drop out immediately when we go to
			// scan those.
			for oblet := b + maxObletBytes; oblet < s.base()+s.elemsize; oblet += maxObletBytes {

    DCHECK(!must_compile_);
    Tensor compilation_key(cpu_allocator, DT_STRING, TensorShape({}));
    Tensor compilation_successful(cpu_allocator, DT_BOOL, TensorShape({}));
    compilation_successful.scalar<bool>()() = false;
    ctx->set_output(0, compilation_key);
    ctx->set_output(1, compilation_successful);
    return;
  }

  // Each execution of an XlaCompile op creates a new ExecutableClosure, even

            .getDimSize(0),
        builder, length.getLoc());
    length.getLength().replaceAllUsesWith(dim);
  } else {
    auto current_size = it->getSecond().size;
    // Reshapes the R1 length to a scalar.
    auto reshape = builder.create<TF::ReshapeOp>(
        length.getLoc(),
        ArrayRef<Type>{RankedTensorType::get(
            {}, getElementTypeOrSelf(current_size.getType()))},

  // node -- a NoOp node that only the Sink node control depends on.
  bool IsSinkLike(const Cluster& cluster);

  // Returns true if `cluster` looks like an "i++" operation on an integer
  // scalar resource variable.
  bool IsScalarIntegerResourceOperation(const Cluster& cluster);

  // ---------------------------------------------------------------------------

					if s.Type == "array" || s.Type == "object" {
						allErrs.SchemaErrors = append(allErrs.SchemaErrors, field.Invalid(fldPath.Child("items").Child("properties").Key(k).Child("type"), schema.Items.Schema.Type, "must be a scalar type if parent array's x-kubernetes-list-type is map"))
					}
				} else {

      auto input_type =
          mlir::dyn_cast<RankedTensorType>(op.getOperand(i).getType());
      auto scalar = RankedTensorType::get({}, input_type.getElementType());
      expected_callee_input_types.push_back(scalar);
      expected_callee_input_types.push_back(scalar);
    }
    FunctionType expected_callee_type = mlir::FunctionType::get(
        op->getContext(), expected_callee_input_types,

	verifyGCBits(t, TypeOf([1]Xptrscalar{}), lit(1))
	verifyGCBits(t, ArrayOf(1, Tptrscalar), lit(1))
	verifyGCBits(t, TypeOf([2]Xscalar{}), empty)
	verifyGCBits(t, ArrayOf(2, Tscalar), empty)
	verifyGCBits(t, TypeOf([10000]Xscalar{}), empty)
	verifyGCBits(t, ArrayOf(10000, Tscalar), empty)
	verifyGCBits(t, TypeOf([2]Xptr{}), lit(1, 1))
	verifyGCBits(t, ArrayOf(2, Tptr), lit(1, 1))

        Keras training loops like `fit`/`evaluate`, the unreduced vector loss is
        passed to the optimizer but the reported loss will be a scalar value.
    *   `SUM`: Scalar sum of weighted losses. 4. `SUM_OVER_BATCH_SIZE`: Scalar
        `SUM` divided by number of elements in losses. This reduction type is
        not supported when used with `tf.distribute.Strategy` outside of

                "batch dimensions of params must be equal to batch dimensions "
                "of indices");
    }
  }

  if ((indices_rank == 0) || (indices_rank == batch_dims_i)) {
    // Scalar indices (output is rank(params) - 1).
    // Erase shape[axis]
    shape.erase(shape.begin() + axis_i);
  } else if (indices_rank == 1) {
    // Vector indices (output is rank(params)).

// From http://software.intel.com/en-us/articles
// (look for improving-the-performance-of-the-secure-hash-algorithm-1)
// This implementation is 2x unrolled, and interleaves vector instructions,
// used to precompute W, with scalar computation of current round
// for optimal scheduling.

// Trivial helper macros.
#define UPDATE_HASH(A,TB,C,D,E) \
	ADDL	(R9), A \
	MOVL	A, (R9) \
	ADDL	4(R9), TB \
	MOVL	TB, 4(R9) \