- mikedanese
  sig-auth-authorizers-reviewers:
    - deads2k
    - dims
    - enj
    - liggitt
    - mikedanese
    - ncdc
    - smarterclayton
    - sttts
    - thockin
    - wojtek-t
  sig-auth-certificates-approvers:
    - liggitt
    - mikedanese
    - smarterclayton
  sig-auth-certificates-reviewers:
    - deads2k
    - dims
    - enj
    - liggitt
    - mikedanese

  %1 = stablehlo.transpose %arg0, dims = [0, 3, 1, 2] : (tensor<1x3x3x4xf32>) -> tensor<1x4x3x3xf32>
  %2 = stablehlo.add %1, %0 : tensor<1x4x3x3xf32>
  return %2 : tensor<1x4x3x3xf32>
}
// CHECK-SAME: (%[[ARG_0:.+]]: tensor<1x3x3x4xf32>) -> tensor<1x4x3x3xf32>
// CHECK-DAG: %[[CONST_0:.+]] = stablehlo.constant

// CHECK-DAG: %[[BIAS_CONST:.+]] = stablehlo.constant {{.*}} : tensor<4xf32>
// CHECK-DAG: %[[BROADCAST_IN_DIM:.+]] = stablehlo.broadcast_in_dim %[[BIAS_CONST]], dims = [1] : (tensor<4xf32>) -> tensor<1x4x5x5xf32>
// CHECK-DAG: %[[TRANSPOSE_0:.+]] = stablehlo.transpose %[[ARG]], dims = [0, 2, 3, 1] : (tensor<1x2x5x5xf32>) -> tensor<1x5x5x2xf32>

options:
  # make root approval non-recursive
  no_parent_owners: true
reviewers:
  - aojea
  - bentheelder
  - cheftako
  - dims
  - justaugustus
  - liggitt
  - wojtek-t
approvers:
  - aojea
  - bentheelder
  - cheftako
  - dims
  - liggitt
  - wojtek-t
emeritus_approvers:
  - mikedanese
  - spiffxp
  - eparis
  - roberthbailey # 2019-03-08
  - jbeda

// CHECK:           %[[TPOS:.*]] = stablehlo.transpose %[[PAD]], dims = [0, 3, 1, 2]
// CHECK:               : (tensor<1x114x114x64xf32>) -> tensor<1x64x114x114xf32>
// CHECK:           return %[[TPOS]] : tensor<1x64x114x114xf32>

func.func @commute_transpose_pad(
      %arg0: tensor<1x112x112x64xf32>, %padding_val: tensor<f32>)
      -> tensor<1x64x114x114xf32> {
  %tspos = stablehlo.transpose %arg0, dims = [0, 3, 1, 2]

namespace mlir {
namespace TF {

// Verifies an reduction op's `input` and reduction `dims`.
LogicalResult VerifyReductionInputAndDims(Value input, Value dims,
                                          Location loc) {
  auto dims_type = mlir::dyn_cast<RankedTensorType>(dims.getType());
  if (!dims_type) return success();
  if (dims_type.getRank() > 1)

    absl::string_view node_shapes_str) {
  std::vector<int> dims;
  for (absl::string_view dim_str : absl::StrSplit(node_shapes_str, ',')) {
    // Treats empty input shape as scalar
    if (dim_str.empty()) continue;
    if (dim_str == "?") {
      dims.push_back(-1);
      continue;
    }
    int size;
    TF_RET_CHECK(absl::SimpleAtoi(dim_str, &size));
    dims.push_back(size);
  }
  return dims;
}

    for (int i = 0; i < n; ++i) {
      result.push_back(i + begin);
    }
    return result;
  };

  // We only support the mhlo.dot style input layouts, i.e.:
  //   lhs: [batch, other dims, contract dims]
  //   rhs: [batch, contract dims, other dims]
  auto dim_nums = dot.getDotDimensionNumbers();
  int batch_dims_count = dim_nums.getLhsBatchingDimensions().size();
  int contracting_dims_count = dim_nums.getLhsContractingDimensions().size();

  auto dims_type =
      RankedTensorType::get(/*shape=*/{1}, rewriter.getIntegerType(64));
  auto dims = DenseIntElementsAttr::get(dims_type, {feature_dim});
  if (shape_value) {
    return rewriter.createOrFold<mhlo::DynamicBroadcastInDimOp>(
        loc, result_type, value1d, shape_value, dims);
  }
  assert(result_type.hasStaticShape());
  return rewriter.create<mhlo::BroadcastInDimOp>(loc, result_type, value1d,

    %11 = stablehlo.convert %3 : (tensor<1x1x3xi32>) -> tensor<1x1x3xf32>
    %12 = stablehlo.broadcast_in_dim %11, dims = [0, 1, 2] : (tensor<1x1x3xf32>) -> tensor<1x4x3xf32>  // Optional
    %13 = stablehlo.subtract %10, %12 : tensor<1x4x3xf32>  // Precalculated zp_neg.
    %14 = stablehlo.broadcast_in_dim %4, dims = [0, 1, 2] : (tensor<1x1x3xf32>) -> tensor<1x4x3xf32>  // Optional
    %15 = stablehlo.multiply %13, %14 : tensor<1x4x3xf32>  // s1 * s2