auto filter_ty = mlir::dyn_cast<RankedTensorType>(op.getFilter().getType());

    // With the exception of out_backprop's batch dimension, out_backprop and
    // filter need to have static shape. Filter is validated here, out_backprop
    // is mostly validated at use.
    if (!out_backprop_ty || !filter_ty || !filter_ty.hasStaticShape())
      return failure();

  // expected-error @+1 {{requires out_backprop operand to have rank at least two with `NHWC` data format}}
  %0 = "tf.BiasAddGrad"(%arg0) {data_format = "NHWC"} : (tensor<3xf32>) -> tensor<3xf32>
  func.return %0 : tensor<3xf32>
}

// -----

func.func @testBiasAddGrad(%arg0: tensor<2x3xf32>) -> tensor<3xf32> {
  // expected-error @+1 {{requires out_backprop operand to have rank at least three with `NCHW` data format}}