// a customizable variant of SHAKE128.
// N is used to define functions based on cSHAKE, it can be empty when plain cSHAKE is
// desired. S is a customization byte string used for domain separation - two cSHAKE
// computations on same input with different S yield unrelated outputs.
// When N and S are both empty, this is equivalent to NewShake128.
func NewCShake128(N, S []byte) ShakeHash {
	if len(N) == 0 && len(S) == 0 {
		return NewShake128()

// Copyright 2020 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

// This file implements helper functions for scope position computations.

package syntax

// StartPos returns the start position of n.
func StartPos(n Node) Pos {
	// Cases for nodes which don't need a correction are commented out.
	for m := n; ; {
		switch n := m.(type) {
		case nil:

// Creates a pass that lifts operations on external resource variables from
// device computation nested in `tf_device::LaunchOp` out so that resource
// variable load operations are all before device computation while resource
// variable store operations are all after device computation. After this pass,
// device computation no longer interacts with external resource variables.

	atomic.StoreInt64(&connectionNumber, 0)
}

// initProxyMetadata initializes just the basic metadata of a proxy. This is decoupled from
// initProxyState such that we can perform authorization before attempting expensive computations to
// fully initialize the proxy.
func (s *DiscoveryServer) initProxyMetadata(node *core.Node) (*model.Proxy, error) {
	meta, err := model.ParseMetadata(node.Metadata)
	if err != nil {

      %computation = "tf_device.cluster_func"(%read) {func = @computation, use_spmd_for_xla_partitioning = true} : (tensor<i32>) -> tensor<i32>
      "tf.AssignVariableOp"(%partitioned_variable, %computation) : (tensor<!tf_type.resource<tensor<i32>>>, tensor<i32>) -> ()
      return
    }

    func @computation(%arg0: tensor<i32>) -> tensor<i32> {
      return %arg0: tensor<i32>

    output_dims.insert(output_dims.begin() + axis, depth);

    Location loc = op.getLoc();

    // The iota result is the effective output shape of the computation,
    // and indices must be broadcast into it. At this point, this computation
    // would need to be reworked quite a bit to support dynamic shapes, so
    // just using static broadcasting.
    auto index_type =

  let description = [{
This operation holds a replicated output from a `tpu.replicate()` computation subgraph.
Each replicated output has the same shape and type alongside the input.

For example:
```
%computation = "tf.Computation"()
%replicated_output:2 = "tf.TPUReplicatedOutput"(%computation)
```

  }

  if (output_to_input_alias.empty()) return absl::OkStatus();

  xla::HloModuleProto* module_proto =
      compilation_result->computation->mutable_proto();
  absl::StatusOr<xla::ProgramShape> program_shape_or_status =
      compilation_result->computation->GetProgramShape();
  TF_RET_CHECK(program_shape_or_status.ok());

  xla::ProgramShape& program_shape = program_shape_or_status.value();

* Introducing `tf.types.experimental.AtomicFunction` as the fastest way to perform TF computations in Python.

    * Can be accessed through `inference_fn` property of `ConcreteFunction`s
    * Does not support gradients.
    * See `tf.types.experimental.AtomicFunction` documentation for how to call and use it.

 * contextual module to provide declarations differently, such as providing alternative declarations for an outsider module. Some
 * functionality such as package set computation may also depend on the contextual module, as the declaration provider may require
 * additional information not available in the [scope].
 */