int channel_dim = (channel_first) ? 1 : shape.dims() - 1;
  TensorShape scale_shape({shape.dim_size(channel_dim)});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto scale = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(scale_shape));
  auto offset = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(scale_shape));
  auto mean = ops::ZerosLike(scope_, scale);
  auto var = ops::OnesLike(scope_, scale);

      rewriter.getBoolAttr(false));

  bool out_reshape_need = (reshape_shape.size() != matmul_shape.size() ||
                           original_type.getRank() != matmul_shape.size());
  // Always add reshape for concrete output shapes.
  if (succeeded(VerifyShapeOfReshapeOp(reshape_shape))) {
    out = createReshapeOp(out, reshape_shape, original_type.getElementType(),
                          op.getLoc(), &rewriter);

                             input_vector_type.getElementType();
    bool same_shape =
        output_tensor_type.getShape() == input_vector_type.getShape();
    if (!same_element_type || !same_shape) {
      op.emitError("input and output should have same shape and element type.");
    }
    return success(same_element_type && same_shape);
  }

  op.emitError("input can not be converted to an output tensor.");