}
            }
        """
    }

    def "detects input file change just before the task is executed"() {
        def inputFile = file("input.txt")
        buildFile << """
            def inputFile = file("input.txt")
            def outputFile = file("build/output.txt")

            task consumer {
                inputs.file(inputFile)
                outputs.file(outputFile)
                doLast {

ABSL_DEPRECATED(
    "Please use the other overload of this function which accepts structured "
    "inputs instead of strings")
// Converts a TensorFlow GraphDef contained in `input` param into a MLIR module.
// Creates MLIR entities into the given MLIR `context`.
absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>>
GraphdefToMlirTranslateFunction(
    llvm::StringRef input, absl::string_view input_arrays,

                  absl::AbortedError("The input `min` must have float type."));
      OP_REQUIRES(context, context->input_type(i * 3 + 1) == DT_FLOAT,
                  absl::AbortedError("The input `max` must have float type."));
      OP_REQUIRES(
          context, context->input_type(i * 3 + 2) == DT_INT64,
          absl::AbortedError("The input `histogram` must have int64 type."));
    }
  }

    const toco::ModelFlags& model_flags, toco::TocoFlags& toco_flags,
    const GraphDebugInfo& debug_info, const GraphDef& input,
    std::string* result) {
  using ::tflite::optimize::ReducedPrecisionSupport;
  mlir::MLIRContext context;
  GraphImportConfig specs;
  mlir::quant::QuantizationSpecs quant_specs;

  // Parse input arrays.
  std::vector<std::string> node_names;
  std::vector<std::string> node_dtypes;

extern const char* const kXlaClusterAttr;

// The attribute that marks certain inputs to a Node as required to be a
// constant at compile time.  If this attribute is present then the
// CompileTimeConstantInput information in the corresponding XlaOpKernel is
// ignored.
//
// The value for this attribute, if present, has to be a list of strings naming
// the inputs to the node that must be constant.

    $metadata is a serialized TPUCompileMetadataProto describing the shapes and types of the inputs to the computation, as well as a mapping onto the TPU pod topology.
    $constant_operand_indices are the indices of the inputs that are constant to the TPU program (e.g. weights in inference), the rest of the inputs are input tensors.
    constant_operand_indices is sorted in ascending order.

                def inputDir = file("dir1").toPath()
                def outputFile = file("build/file1.txt").toPath()
                def outputDir = file("build/dir1").toPath()
                inputs.file(inputFile)
                inputs.dir(inputDir)
                outputs.file(outputFile)
                outputs.dir(outputDir)
                doLast {
                    Files.createDirectories(outputFile.parent)

            // TODO when implemented, this assertion should be replaced with the commented one:
            inputs.expect(allOf(startsWith("Plugin class 'SneakyPluginA': file '${FileUtils.testFileName}'"), containsString(FileUtils.testFileName)))
            /*
            pluginClasses.forEach {
                inputs = inputs.expect(allOf(startsWith("Plugin class '$it':"), containsString(FileUtils.testFileName))))
            }*/
        }

      cost += GetRankedTensorSize(type);
    } else {
      // For unranked tensors, use the max size among the input tensors. This is
      // because the only dynamic information of the function should be the
      // input, so the size of dynamic tensors should be usually capped by
      // inputs' sizes.
      cost += max_arg_size_;
    }
  }

  cost_map_[op] = cost;
}

}  // namespace tfrt_compiler

        buildFile << """
// This is a workaround to bust the JVM's file canonicalization cache
def f = file("delete-me")
f.createNewFile()
f.delete() // invalidates cache

task work {
    inputs.file('in.txt')
    inputs.dir('in-dir')
    def outTxt = file('out.txt')
    def outDir = file('out-dir')
    outputs.file(outTxt)
    outputs.dir(outDir)
    doLast {
        outTxt.text = 'content'