data_rank = then_tensor.getRank();
    if (then_tensor.getRank() > 0)
      data_first_dim = then_tensor.getShape().front();
    if (else_tensor.getRank() > 0)
      data_first_dim = std::max(else_tensor.getShape().front(), data_first_dim);
  } else if (then_has_rank) {
    data_rank = then_tensor.getRank();
    if (then_tensor.getRank() > 0)
      data_first_dim = then_tensor.getShape().front();

  if (!output_ty) return success();

  int64_t expected_output_rank = std::max(x_ty.getRank(), y_ty.getRank());
  if (output_ty.getRank() != expected_output_rank) {
    return op.emitOpError()
           << "found invalid output rank, expected " << expected_output_rank
           << " but got " << output_ty.getRank();
  }

  // Check output batch dim with potential broadcasting.

  if (!output_ty) return success();

  int64_t expected_output_rank = std::max(x_ty.getRank(), y_ty.getRank());
  if (output_ty.getRank() != expected_output_rank)
    return op.emitOpError()
           << "found invalid output rank, expected " << expected_output_rank
           << " but got " << output_ty.getRank();

  // Check output batch dim with potential broadcasting.

  return lhs.hasRank() && rhs.hasRank() && lhs.getRank() == rhs.getRank();
}

// Creates a compatible RankedTensorType where mismatched dimensions are
// replaced with dynamic sizes.
RankedTensorType GetCompatibleRankedTensorType(RankedTensorType lhs,
                                               RankedTensorType rhs) {
  assert(lhs.getRank() == rhs.getRank());
  llvm::SmallVector<int64_t, 4> dims;

    auto reshape_input_ty = mlir::dyn_cast<ShapedType>(reshape_input.getType());
    if (!reshape_input_ty.hasStaticShape() || input_ty.getRank() == 0 ||
        reshape_input_ty.getRank() == 0 ||
        input_ty.getDimSize(input_ty.getRank() - 1) !=
            reshape_input_ty.getDimSize(reshape_input_ty.getRank() - 1)) {
      return failure();
    }

    // Connect the input to the one of reshape.

    // before broadcasting.
    if (operand_type.getRank() < output_type.getRank()) {
      input = InsertExpandDimsOp(op, rewriter, input, output_type.getRank());
    }

    SmallVector<int32_t> broadcast_shape =
        CastI64ArrayToI32(output_type.getShape()).value();
    TensorType broadcast_shape_type =
        output_type.cloneWith({output_type.getRank()}, rewriter.getI32Type());
    auto broadcast_shape_attr =

  auto lhs_type = mlir::cast<ShapedType>(lhs.getType());
  auto rhs_type = mlir::cast<ShapedType>(rhs.getType());
  const int lhs_rank = lhs_type.getRank();
  const int rhs_rank = rhs_type.getRank();
  ImplicitLocOpBuilder builder(loc, rewriter);

  // Collects lhs and rhs dimensions information.
  DotDimensionsInfo lhs_dot_dimensions_info(

      ").getType().cast<ShapedType>().getRank() >= " # m>]>>;

class TFL_OperandHasRankRange<int n, int x, int y> :
  PredOpTrait<"operand " # n # " has rank range [" # x # ", " # y # "]",
    Or<[TFL_OperandIsUnrankedPred<n>,
      CPred<"$_op.getOperand(" # n # ").getType().cast<ShapedType>().getRank() "
      ">= " # x # " && $_op.getOperand(" # n # ").getType().cast<ShapedType>()."
      "getRank() <= " # y>]>>;

Method <org.gradle.api.ActionConfiguration.getParams()> does not have raw return type assignable to org.gradle.api.provider.Property in (ActionConfiguration.java:0)
Method <org.gradle.api.AntBuilder.getAnt()> does not have raw return type assignable to org.gradle.api.provider.Provider in (AntBuilder.java:0)
Method <org.gradle.api.AntBuilder.getLifecycleLogLevel()> does not have raw return type assignable to org.gradle.api.provider.Property in (AntBuilder.java:0)

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