def "detects missing dependency between two tasks and fails (#description)"() {
        buildFile """
            task producer {
                def outputFile = file("${producedLocation}")
                outputs.${outputType}(${producerOutput == null ? 'outputFile' : "'${producerOutput}'"})
                doLast {
                    outputFile.parentFile.mkdirs()
                    outputFile.text = "produced"
                }
            }

    }

    private String findLineThatContains(String text, LogContent content, String outputType) {
        Optional<String> foundLine = content.getLines().stream()
            .filter(line -> line.contains(text))
            .findFirst();
        return foundLine.orElseGet(() -> {
            failOnMissingOutput("Did not find expected text in " + outputType, "Build output", text, text);
            // never returned
            return "";

		return resultError(issues.String(), apiservercel.ErrorTypeInvalid)
	}
	// type checker does not require the program, however the type must still be set.
	return plugincel.CompilationResult{
		OutputType: ast.OutputType(),
	}
}

std::unique_ptr<OperationPass<ModuleOp>> CreateOptimizeFunctionalOpsPass();

std::unique_ptr<OperationPass<func::FuncOp>> CreateModifyIONodesPass(
    mlir::Type input_type, mlir::Type output_type);

std::unique_ptr<OperationPass<func::FuncOp>> CreateModifyIONodesPass();

// Creates an instance of the TensorFlow Lite dialect PostQuantizeRemoveQDQ
// pass.

    int missing_ctx_input_prefix,
    const xla::HloInputOutputAliasConfig& input_output_alias,
    absl::Span<const int> input_mapping,
    const std::map<int, const Tensor*>& resource_vars_snapshots,
    DataType output_dtype, const TensorShape& output_shape,
    Allocator* output_allocator, bool allocate_xla_tensors, se::Stream* stream,
    bool use_multiple_streams, std::shared_ptr<se::Event> definition_event) {

    Node* placeholder_node;
    if (iter == placeholders.end()) {
      NodeDefBuilder placeholder_builder(new_name, "Placeholder");
      placeholder_builder.Attr("dtype", src->output_type(src_output));
      string outside_compilation_attr;
      TF_RETURN_IF_ERROR(GetNodeAttr(dst->attrs(),
                                     outside_compilation_attr_name,

  out_data_type: TensorType;
}

table DequantizeOptions {
}

table MaximumMinimumOptions {
}

table TileOptions {
}

table ArgMaxOptions {
  output_type : TensorType;
}

table ArgMinOptions {
  output_type : TensorType;
}

table GreaterOptions {
}

table GreaterEqualOptions {
}

table LessOptions {
}

table LessEqualOptions {
}

	1, // 1: istio.networking.nds.v1.NameTable.TableEntry.value:type_name -> istio.networking.nds.v1.NameTable.NameInfo
	2, // [2:2] is the sub-list for method output_type
	2, // [2:2] is the sub-list for method input_type
	2, // [2:2] is the sub-list for extension type_name
	2, // [2:2] is the sub-list for extension extendee
	0, // [0:2] is the sub-list for field type_name
}

#include "tensorflow/core/util/debug_data_dumper.h"

using tensorflow::errors::InvalidArgument;

namespace tensorflow {
namespace {

Status ValidateNonRefOutput(const Node* node, int idx) {
  const DataType& dt = node->output_type(idx);
  return IsRefType(dt)
             ? InvalidArgument("Output ", idx, " of node '", node->name(),
                               "' has a reference type ", DataTypeString(dt))
             : absl::OkStatus();
}

  out_data_type: TensorType;
}

table DequantizeOptions {
}

table MaximumMinimumOptions {
}

table TileOptions {
}

table ArgMaxOptions {
  output_type : TensorType;
}

table ArgMinOptions {
  output_type : TensorType;
}

table GreaterOptions {
}

table GreaterEqualOptions {
}

table LessOptions {
}

table LessEqualOptions {
}