// the shape inference pass is run early in the pass pipeline, shape inference
  // during import is not necessary.
  config.enable_shape_inference = false;
  // Some graphs may require _output_shapes (an unregistered attribute)
  // to override shapes. It is unfortunately not always set correctly so only
  // do it optionally.
  config.unconditionally_use_set_output_shapes =

  let summary = "Transpose convolution operator";

  let description = [{
    Performs transpose convolution operation on input.
  }];

  let arguments = (ins
    TFL_I32Tensor:$output_shape,
    TFL_TensorOf<[F32, QI8, QUI8, QI16]>:$weights,
    TFL_TensorOf<[F32, QI8, QUI8, QI16]>:$input,
    TFL_TensorOfOrNone<[F32, QI32, I64]>:$bias,
    TFL_PaddingAttr:$padding,

func.func @testUnimplementedOp() -> (tensor<i32>, tensor<i32>) {
  %0 = arith.constant dense<1> : tensor<i32>
  %1 = arith.constant dense<2> : tensor<i32>
  %2 = "tf.Maximum"(%0, %1) {_output_shapes = ["tfshape$"]} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  %3 = "tf.Minimum"(%0, %1) {random_attr = "hello"} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  func.return %2, %3: tensor<i32>, tensor<i32>

  // CHECK-DAG: [[LINSPACE:%.*]] = chlo.broadcast_add [[MUL]], [[START]] {broadcast_dimensions = array<i64>}
  // CHECK: return [[LINSPACE]]
  %0 = "tf.Const"() {_output_shapes = ["tfshape$"], device = "", dtype = i32, value = dense<4> : tensor<i32>} : () -> tensor<i32>
  %1 = "tf.LinSpace"(%arg0, %arg1, %0) : (tensor<f32>, tensor<f32>, tensor<i32>) -> tensor<4xf32>
  func.return %1 : tensor<4xf32>

                    IsWithinInt32Range);

    if (elements_all_in_int32_range) {
      std::vector<int32_t> output_shape(output_ty.getRank());
      std::transform(output_ty.getShape().begin(), output_ty.getShape().end(),
                     output_shape.begin(),
                     [](int64_t val) { return static_cast<int32_t>(val); });
      output_int_type = tensorflow::GetTypeFromTFTensorShape(

        all the TensorFlow OPs included in each of the `TRTEngineOp`s.

*   `tf.tpu.experimental.embedding`:

    *   `tf.tpu.experimental.embedding.FeatureConfig` now takes an additional
        argument `output_shape` which can specify the shape of the output
        activation for the feature.
    *   `tf.tpu.experimental.embedding.TPUEmbedding` now has the same behavior