s: "loc:@a"
          }
        }
      }
      attr {
        key: "_output_shapes"
        value {
          list {
            shape {
            }
          }
        }
      }
    }
    node {
      name: "b/initial_value"
      op: "Const"
      attr {
        key: "_output_shapes"
        value {
          list {
            shape {
            }
          }

      end++;
    if (begin != end) {
      mlir::TF::ResultShapeRange output_shapes = {
          mlir::TF::ResultShapeIterator(begin),
          mlir::TF::ResultShapeIterator(end)};
      SetShapeAttribute("_output_shapes", output_shapes, attributes);
    }
  }

  return absl::OkStatus();
}

// HLO-level inputs are supplied, and HLO-level outputs are produced.
// xla_params is the HLO-level inputs and returns is the HLO-level outputs.
// If unconditionally_use_output_shapes is true then the unregistered
// attribute _output_shapes is always used to set the output shapes of the ops.
ABSL_DEPRECATED(
    "Use v1/compile_tf_graph.h::CompileTensorflowGraphToHlo instead.")
Status BuildHloFromGraph(
    const Graph& graph, xla::XlaBuilder& builder,

      (*node_def->mutable_attr())["_handle_dtypes"] = handle_dtypes_attr;
      (*node_def->mutable_attr())["_handle_shapes"] = handle_shapes_attr;
    }
  }

  TF_RETURN_IF_ERROR(
      SetShapeAttribute("_output_shapes", arg_type, node_def->mutable_attr()));

  DataType dtype;
  TF_RETURN_IF_ERROR(ConvertToDataType(arg_type.getElementType(), &dtype));
  AttrValue type_attr;
  type_attr.set_type(dtype);

      (*node_def->mutable_attr())["_handle_dtypes"] = handle_dtypes_attr;
      (*node_def->mutable_attr())["_handle_shapes"] = handle_shapes_attr;
    }
  }

  TF_RETURN_IF_ERROR(
      SetShapeAttribute("_output_shapes", arg_type, node_def->mutable_attr()));

  DataType dtype;
  TF_RETURN_IF_ERROR(ConvertToDataType(arg_type.getElementType(), &dtype));
  AttrValue type_attr;
  type_attr.set_type(dtype);

  // 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 =

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>

  SmallVector<int64_t, 2> output_shape{1, mlir::ShapedType::kDynamic};
  EXPECT_EQ(mlir::cast<RankedTensorType>(output_types[0]).getShape().size(),
            output_shape.size());
  for (int i = 0; i < output_shape.size(); i++) {
    EXPECT_EQ(mlir::cast<RankedTensorType>(output_types[0]).getDimSize(i),
              output_shape[i]);
  }
}

    %drop_remainder: i1 {tfr.name="drop_remainder"},
    %f: !tfr.attr {tfr.name="func"},
    %output_types: !tfr.attr {tfr.name="output_types"},
    %output_shapes: !tfr.attr {tfr.name="output_shapes"},
    %preserve_cardinality: i1 {tfr.name="preserve_cardinality", tfr.default=false}) -> !tfr.tensor {
  %batch = "tfr.constant_tensor"(%batch_size) : (i64) -> tensor<i64>

    CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);
    CHECK_EQ(output_shapes->num_items, 1);

    int num_dims = output_shapes->items[0].num_dims;
    int64_t* dims = output_shapes->items[0].dims;

    if (!expected_shape.has_value()) {
      EXPECT_EQ(num_dims, -1);
      EXPECT_EQ(dims, nullptr);
      return;
    }

    EXPECT_EQ(num_dims, expected_shape->size());