let results = (outs TFL_TensorOf<[F32, QI8, QI16]>:$y);

  // This builder doesn't work with quantized type, so it can only be used by
  // non-quantization tablegen patterns. Currently, it is used by the
  // elementwise-move reordering pattern in the optimize_patterns.td
  let builders = [
    OpBuilder<(ins "Value":$input),
    [{
      $_state.addOperands({input});

HSPLokhttp3/Request$Builder;-><init>()V
HSPLokhttp3/Request$Builder;-><init>(Lokhttp3/Request;)V
HSPLokhttp3/Request$Builder;->build()Lokhttp3/Request;
HSPLokhttp3/Request$Builder;->header(Ljava/lang/String;Ljava/lang/String;)Lokhttp3/Request$Builder;
HSPLokhttp3/Request$Builder;->method(Ljava/lang/String;Lokhttp3/RequestBody;)Lokhttp3/Request$Builder;
HSPLokhttp3/Request$Builder;->url(Ljava/lang/String;)Lokhttp3/Request$Builder;

    q_params = tflite::CreateQuantizationParameters(
        builder_, builder_.CreateVector<float>(mins),
        builder_.CreateVector<float>(maxs));
  }
  return tflite::CreateTensor(
      builder_, builder_.CreateVector(shape), tflite_element_type,
      /*buffer=*/0, builder_.CreateString(name), q_params,
      /*is_variable=*/false, /*sparsity=*/0, /*shape_signature=*/0,

                                               Builder *builder) {
  RankedTensorType ty = mlir::RankedTensorType::get(
      {static_cast<int32_t>(values.size())}, builder->getIntegerType(32));
  return DenseIntElementsAttr::get(ty, values);
}

DenseIntElementsAttr GetI64ElementsAttr(ArrayRef<int64_t> values,
                                        Builder *builder) {
  RankedTensorType ty = RankedTensorType::get(

  // and a return operation to return the Graph results.
  builder_.setInsertionPointToEnd(&graph_op.getBody().front());
  builder_.create<mlir::tf_executor::FetchOp>(graph_op.getLoc(),
                                              inst_to_return);
  builder_.setInsertionPointToEnd(bb);
  builder_.create<mlir::func::ReturnOp>(mlir::UnknownLoc::get(context_),

      RankedTensorType::get(static_cast<int32_t>(1), builder.getI32Type());
  auto out_segids_cst = builder.create<TF::ConstOp>(
      builder.getI32TensorAttr(flattened_out_segids));
  auto contracting_segids_cst = builder.create<TF::ConstOp>(
      builder.getI32TensorAttr(flattened_contracting_segids));
  auto num_segids_tensor =
      builder.create<TF::ConstOp>(builder.getI32IntegerAttr(1));

	pack(b *Builder, a *Action, afile string, ofiles []string) error
	// ld runs the linker to create an executable starting at mainpkg.
	ld(b *Builder, root *Action, targetPath, importcfg, mainpkg string) error
	// ldShared runs the linker to create a shared library containing the pkgs built by toplevelactions
	ldShared(b *Builder, root *Action, toplevelactions []*Action, targetPath, importcfg string, allactions []*Action) error

      op.getOperation());
}

void EqualOp::build(OpBuilder& builder, OperationState& result, Value x,
                    Value y, BoolAttr incompatible_shape_error) {
  auto result_type = DeduceEqualCmpOpType(&builder, result.location, x, y,
                                          incompatible_shape_error);
  return build(builder, result, result_type, x, y, incompatible_shape_error);
}

namespace {

      op.getOperation());
}

void NotEqualOp::build(OpBuilder &builder, OperationState &result, Value x,
                       Value y, BoolAttr incompatible_shape_error) {
  auto result_type = DeduceEqualCmpOpType(&builder, result.location, x, y,
                                          incompatible_shape_error);
  return build(builder, result, result_type, x, y, incompatible_shape_error);
}

  }

  // Otherwise, build a new concatenation op with non-empty values.
  mlir::OpBuilder builder(getOperation());
  auto new_concat = builder.create<TFL::ConcatenationOp>(
      getLoc(), getType(), non_empty_values,
      builder.getIntegerAttr(builder.getIntegerType(32), getAxis()),
      builder.getStringAttr(getFusedActivationFunction()));
  return new_concat.getResult();
}