TF_Operation* RandomUniform(TF_Operation* shape, TF_DataType dtype,
                            TF_Graph* graph, TF_Status* s);

// Split `input` along the first dimension into 3 tensors
TF_Operation* Split3(TF_Operation* input, TF_Graph* graph, TF_Status* s,
                     const char* name = "split3");

bool IsPlaceholder(const tensorflow::NodeDef& node_def);

bool IsScalarConst(const tensorflow::NodeDef& node_def, int v);

       {{func_op, 0}, {func_op, 1}}, {{1, 2}, {5, 6}});
  VerifyFDef({"split3_const0", "split3_1"}, M({{"feed"}}),
             M({{"split3"}, {"split3_0"}}),
             {{"split3_const0:output:0", "split3_1:0"},
              {"feed", "split3_1:1"},
              {"split3_1:output:0", "split3"},
              {"split3_1:output:2", "split3_0"}},
             {});
}

TEST_F(CApiFunctionTest, UsingTwoOutputsOfSplitAsInputs) {

  TF_OperationDescription* desc = TF_NewOperation(graph, "Split", name);
  TF_AddInput(desc, {zero, 0});
  TF_AddInput(desc, {input, 0});
  TF_SetAttrInt(desc, "num_split", 3);
  TF_SetAttrType(desc, "T", TF_INT32);
  // Set device to CPU since there is no version of split for int32 on GPU
  // TODO(iga): Convert all these helpers and tests to use floats because

// gcc -gsplit-dwarf split.c -o split.elf

int main() 
{

*/

package runtime

import (
	"bytes"
	"io"
)

// Splice is the interface that wraps the Splice method.
//
// Splice moves data from given slice without copying the underlying data for
// efficiency purpose. Therefore, the caller should make sure the underlying
// data is not changed later.
type Splice interface {
	Splice([]byte)
	io.Writer
	Reset()
	Bytes() []byte
}

#$ Used in:
#$ - site-src/guides/traffic-splitting.md
apiVersion: gateway.networking.k8s.io/v1beta1
kind: HTTPRoute
metadata:
  name: simple-split
spec:
  rules:
  - backendRefs:
    - name: foo-v1
      port: 8080
      weight: 90
    - name: foo-v2
      port: 8080

Austin Clements <******@****.***> 1442162945 -0400

      Spliterator<? extends T> splitr = stream.spliterator();
      splitrsBuilder.add(splitr);
      characteristics &= splitr.characteristics();
      estimatedSize = LongMath.saturatedAdd(estimatedSize, splitr.estimateSize());
    }
    return StreamSupport.stream(
            CollectSpliterators.flatMap(
                splitrsBuilder.build().spliterator(),
                splitr -> (Spliterator<T>) splitr,

// RUN: tf-opt -tfl-split-merged-operands %s | FileCheck %s

func.func @testSingleLstm(%arg0: tensor<4x4xf32>, %arg1: tensor<4xf32>, %arg2: tensor<4x4x4xf32>) -> tensor<4x4x4xf32> {
  // CHECK-LABEL: testSingleLstm
  // CHECK-DAG:  %[[CST_0:.*]] = "tfl.pseudo_const"() <{value = dense<0.000000e+00> : tensor<4x4xf32>}> : () -> tensor<4x4xf32>
  // CHECK-DAG:  %[[CST_1:.*]] = "tfl.pseudo_const"() <{value = dense<0.000000e+00> : tensor<4x4xf32>}> : () -> tensor<4x4xf32>

					blocks[argID] = d
					if f.pass.debug > 0 {
						f.Warnl(p.Pos, "split critical edge")
					}
				} else {
					reusedBlock = true
				}
			} else {
				// no existing block, so allocate a new block
				// to place on the edge
				d = f.NewBlock(BlockPlain)
				d.Pos = p.Pos
				if f.pass.debug > 0 {
					f.Warnl(p.Pos, "split critical edge")
				}
			}

			// if this not the first argument for the