var tiles []Tile

	// Plan to fetch tiles necessary to recompute tree hash.
	// If it matches, those tiles are authenticated.
	stx := subTreeIndex(0, r.tree.N, nil)
	stxTileOrder := make([]int, len(stx))
	for i, x := range stx {
		tile, _, _ := tileForIndex(h, x)
		tile = tileParent(tile, 0, r.tree.N)
		if j, ok := tileOrder[tile]; ok {
			stxTileOrder[i] = j
			continue
		}

  }

  return builder->create<mlir::TF::ConcatOp>(
      location, output_type, concat_dimension_op.getOutput(), inputs);
}

// For tile sharded inputs to TPU computation, inject split op between the
// input values and TPU computation so that tiled input values are passed in
// as inputs to TPU computations. If more than one dimension is sharded, then

  %177 = "tf.Reshape"(%176, %9) {device = ""} : (tensor<i32>, tensor<0xi32>) -> tensor<i32>
  %178 = "tf.Pack"(%7, %177) {axis = 0 : i64, device = ""} : (tensor<i32>, tensor<i32>) -> tensor<2xi32>
  %179 = "tf.Tile"(%115, %178) {device = ""} : (tensor<?x1xi64>, tensor<2xi32>) -> tensor<?x?xi64>
  %180 = "tf.Mul"(%177, %118) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i32>

  %multiples = "tf.Const"() { device = "/device:CPU:0", value = dense<[7,3]> : tensor<2xi32> } : () -> tensor<2xi32>
  // CHECK: T = !corert.string
  %output = "tf.Tile"(%arg, %multiples) { device = "/device:CPU:0" } : (tensor<1x2x!tf_type.string>, tensor<2xi32>) -> tensor<?x6x!tf_type.string>
  func.return %output : tensor<?x6x!tf_type.string>
}

// CHECK-LABEL: func @shape

// license that can be found in the LICENSE file.

// Package cpu implements processor feature detection for
// various CPU architectures.
package cpu

import (
	"os"
	"strings"
)

// Initialized reports whether the CPU features were initialized.
//
// For some GOOS/GOARCH combinations initialization of the CPU features depends
// on reading an operating specific file, e.g. /proc/self/auxv on linux/arm

  "TFL::HasOneUseOrUsedByOnlyBinaryOps($0)">>;

// Pattern for skipping Tile if it is mainly for broadcasting and the
// Op is already supporting broadcasting.
multiclass FuseTileBroadcastIntoFollowingBinary<Op BinaryOp> {
  def FuseTileBroadcastToBinaryOp1#BinaryOp : Pat<
    (BinaryOp:$result (TFL_TileOp $input, (Arith_ConstantOp $tile)),
     $operand, $act_func),
    (BinaryOp $input, $operand, $act_func),

	return time.Parse(time.RFC3339, version)
}

// releaseTimeToReleaseTag - converts a time to a string formatted as
// an official MinIO release tag.
//
// An official minio release tag looks like:
// `RELEASE.2017-09-29T19-16-56Z`
func releaseTimeToReleaseTag(releaseTime time.Time) string {
	return "RELEASE." + releaseTime.Format(MinioReleaseTagTimeLayout)
}

  %0 = "tfl.sqrt"(%arg0) : (tensor<2x3xf32>) -> tensor<2x3xf32>
  %1 = "tfl.tile"(%0, %cst) : (tensor<2x3xf32>, tensor<1x2xi32>) -> tensor<2x6xf32>
  %2 = "tfl.add"(%cst1, %1) {fused_activation_function = "NONE"} : (tensor<1x6xf32>, tensor<2x6xf32>) -> tensor<2x6xf32>
  func.return %2 : tensor<2x6xf32>

  // CHECK: %[[TILE:[0-9].*]] = "tfl.tile"
}

// CHECK-LABEL: InvalidFuseTileAlreadyBroadcastAlongTileDim

    const int64_t tensor_rank = ranked_type.getRank();
    int tile_assignment_rank = sharding->tile_assignment_dimensions_size();

    // When a tensor is partial or subgroup tiled, its tile assignment will
    // have one or more dimension(s) than its rank; so, we subtract them to
    // determine which rank the sharding is compatible with.
    tile_assignment_rank -= (int)sharding->replicate_on_last_tile_dim();

  LESS = 58,
  NEG = 59,
  PADV2 = 60,
  GREATER = 61,
  GREATER_EQUAL = 62,
  LESS_EQUAL = 63,
  SELECT = 64,
  SLICE = 65,
  SIN = 66,
  TRANSPOSE_CONV = 67,
  SPARSE_TO_DENSE = 68,
  TILE = 69,
  EXPAND_DIMS = 70,
  EQUAL = 71,
  NOT_EQUAL = 72,
  LOG = 73,
  SUM = 74,
  SQRT = 75,
  RSQRT = 76,
  SHAPE = 77,
  POW = 78,
  ARG_MIN = 79,
  FAKE_QUANT = 80,