added_func_op.setName(builder.getStringAttr(function_name));
  added_call_op.setCallee(builder.getStringAttr(function_name));
}

// Raises partitioned sequential `Operations` from a block to a new function
// definition. `Operations` are partitioned into classes from the cartesian
// product of possible devices and inference datatypes. For example, we might

    Arg<TF_StrTensor, [{shape {N}.  The slice specs of the tensors to be restored.
Empty strings indicate that they are non-partitioned tensors.}]>:$shape_and_slices,
    // VarHandles for restored tensors.
    Variadic<TF_Tensor>:$var_handles,
    // Data types for restored tensor.
    TypeArrayAttr: $restored_dtypes,

  // Operation along with its respective operands and result Values(s).
  // The semantic of `Operation::clone` is copying given entity *into* this
  // entity. The new FuncOp body is populated by cloning partitioned ops into
  // it. Cloning Operation(s) will create cloned Value(s) for the results of a
  // cloned op, but it needs a reference to the new operand Value(s) which are

  // Add TF->TF lowering patterns.
  TF::PopulateTFLoweringBeforeHLOPatterns(context, &legalize_lower_patterns);

  if (tf2xla_fallback_device_type && prefer_tf2xla) {
    VLOG(1) << "TF to XLA legalization patterns are partitioned by op into "
               "either native MLIR legalization, or TF2XLA fallback "
               "legalzation, with a preference toward TF2XLA.";
  } else if (tf2xla_fallback_device_type) {

      FunctionType::get(func.getContext(), new_input_types,
                        func.front().getTerminator()->getOperandTypes()));
}

// Contains cached information for decomposed callee functions for (stateful)
// partitioned call ops.
struct PartitionedCallStackOpsInfo {
  bool signature_change;
  func::FuncOp decomposed_callee;
  llvm::SmallDenseMap<int64_t, int64_t> stack_var_arg_to_size_arg;
};

std::unique_ptr<OperationPass<ModuleOp>> CreateTPUResourceReadForWritePass();

// Creates a pass that reorders partitiioned resource reads and replicated
// inputs.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateTPUReorderReplicateAndPartitionedInputsPass();

// Creates a pass that partitions unpartitioned resource read/write to
// partitioned resource variables.
std::unique_ptr<OperationPass<func::FuncOp>>

pkg net/http, func ParseCookie(string) ([]*Cookie, error) #66008
pkg net/http, func ParseSetCookie(string) (*Cookie, error) #66008
pkg net/http, method (*Request) CookiesNamed(string) []*Cookie #61472
pkg net/http, type Cookie struct, Partitioned bool #62490
pkg net/http, type Cookie struct, Quoted bool #46443
pkg net/http, type Request struct, Pattern string #66405

// in this function should be on the same host.
struct FunctionMetadata {
  // The original function name before partition.
  llvm::StringRef original_name;
  // The insertion point of partition functions.
  Block::iterator insertion_point;
  // The partitioned function name.
  llvm::StringRef partition_name;
  // The input values of the function.
  llvm::SmallVector<Value, 4> inputs;

  let summary =
    "returns `f(inputs)`, where `f`'s body is placed and partitioned.";

  let description = [{
Asynchronously executes a function, potentially across multiple devices but
within a single process. The kernel places and partitions a given function's
underlying graph, and executes each of the partitioned subgraphs as a function.
  }];

  let arguments = (ins
    Variadic<TF_Tensor>:$args,

func.func @cluster_func(%arg0: tensor<*xi32>) -> tensor<*xi32> {
  func.return %arg0 : tensor<*xi32>
}

// -----

// Tests output sharding of unpartitioned resource write takes on same sharding
// as unpartitioned resource.

// CHECK-LABEL: func @partitioned_input_output
func.func @partitioned_input_output(%arg0: tensor<!tf_type.resource<tensor<f32>>>) {