// CHECK-DAG:       [[VAL_11:%.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>
// CHECK:           [[VAL_12:%.*]]:4 = "tf.SplitV"([[VAL_7]], [[VAL_10]], [[VAL_11]]) : (tensor<40x8xf32>, tensor<4xi32>, tensor<i32>) -> (tensor<10x8xf32>, tensor<10x8xf32>, tensor<10x8xf32>, tensor<10x8xf32>)

  func.return
}

// -----

func.func @testSplitVSplitDimOutOfRange(%input: tensor<4x4xf32>, %split_sizes: tensor<2xi32>) {
  %split_dim = "tf.Const"() {value = dense<100>: tensor<i32>} : () -> (tensor<i32>)
  // expected-error @+1 {{split dimension must be in range [-2, 2)}}

  }
  return success();
}

//===----------------------------------------------------------------------===//
// SplitOp
//===----------------------------------------------------------------------===//

// Verifies the input and split dimension operands for tf.Split/tf.SplitV.
// Writes the split dimension's index (adjusted with input rank) via `dim_index`
// if it's a constant.
template <class Op>

0===s&&(s=document),!t||!D(t))return null;var a;At(t=t.replace(Et,"$1 *"))&&(a=[],t=t.match(_t).map(function(t){return t.replace(/,$/,"").trim()}).map(function(t,e){var i=s;if("!"===t[0]){var n=t.substr(1).trim().split(" ");i=Dt(Pt(s),n[0]),t=n.slice(1).join(" ").trim()}if("-"===t[0]){var r=t.substr(1).trim().split(" "),o=(i||s).previousElementSibling;i=Mt(o,t.substr(1))?o:null,t=r.slice(1).join(" ")}return i?(i.id||(i.id="uk-"+Date.now()+e,a.push(function(){return at(i,"id")})),"#"+Lt(i.id)+" "+t):null}).filt...

		},
		workloadAgnostic: true,
	})

	splits := [][]int{
		{50, 25, 25},
		{80, 10, 10},
	}
	if skipVM {
		splits = [][]int{
			{50, 50},
			{80, 20},
		}
	}
	for _, split := range splits {
		split := split
		t.RunTraffic(TrafficTestCase{
			name:           fmt.Sprintf("shifting-%d", split[0]),

  %4, %5 = "tfl.split"(%split_dim_2, %arg0) {num_splits = 2 : i32} : (tensor<1xi32>, tensor<16x4xf32>) -> (tensor<16x2xf32>, tensor<16x2xf32>)
  %6:2 = "tfl.split"(%split_dim_2, %arg0) {num_splits = 2 : i32} : (tensor<1xi32>, tensor<16x4xf32>) -> (tensor<16x2xf32>, tensor<16x?xf32>)
  %7:2 = "tfl.split"(%split_dim_2, %arg0) {num_splits = 2 : i32} : (tensor<1xi32>, tensor<16x4xf32>) -> (tensor<?x2xf32>, tensor<16x?xf32>)

      // CHECK:      %[[SPLIT_0_OUT:[a-z0-9]+]]:2 = "tf.Split"(%[[CONST_SPLIT_0_DIM]], %[[RI_0]])
      // CHECK:      %[[CONST_SPLIT_1_DIM:.*]] = "tf.Const"()
      // CHECK:      %[[SPLIT_1_OUT:[a-z0-9]+]]:2 = "tf.Split"(%[[CONST_SPLIT_1_DIM]], %[[SPLIT_0_OUT]]#0)
      // CHECK:      %[[CONST_SPLIT_2_DIM:.*]] = "tf.Const"()
      // CHECK:      %[[SPLIT_2_OUT:[a-z0-9]+]]:2 = "tf.Split"(%[[CONST_SPLIT_2_DIM]], %[[SPLIT_0_OUT]]#1)

def TPUColocateSplitsPass : Pass<"tf-tpu-colocate-splits", "mlir::func::FuncOp"> {
  let summary = "Colocates each Split op with its predecessor";
  let constructor = "TFTPU::CreateTPUColocateSplitsPass()";
  let description = [{
    It is beneficial for performance to assign a `Split` op to the same device
    as its predecessor. This is because the weight of cut edges is always
    minimized when the `Split` is with its predecessor. This colocation

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{Number|String}\n * Value in pixels, or original string if no values were extracted\n */\nexport function toValue(str, measurement, popperOffsets, referenceOffsets) {\n  // separate value from unit\n  const split = str.match(/((?:\\-|\\+)?\\d*\\.?\\d*)(.*)/);\n  const value = +split[1];\n  const unit = split[2];\n\n  // If it's not a number it's an operator, I guess\n  if (!value) {\n    return str;\n  }\n\n  if (unit.indexOf('%') === 0) {\n    let element;\n    switch (unit) {\n      case '%p':\n      ...