}
}
----
=====
====

== Understanding inputs and outputs

Task inputs and outputs are important for:

1. *Up-to-date checks* - Gradle’s incremental build feature helps your build avoid doing the same work more than once by looking at changes for task inputs and outputs.
2. *Linking task inputs and outputs* - When outputs of one task are linked to the inputs of another, Gradle can automatically create task dependencies.

            logger2.log(it, "Test");
            logger3.log(it, "Test");
        }
        redirector.stop()

        then:
        0 * _

        System.out == outputs.stdOutPrintStream
        System.err == outputs.stdErrPrintStream
    }

    def "start and stop output with redirection and default logging"() {
        when:
        redirector.redirectStandardOutputTo(stdOutListener)

    } else {
      tensor_names.push_back(node_name + ":0");
    }
  }
  std::vector<Tensor> outputs;
  TF_RETURN_IF_ERROR(
      session->Run(/* inputs */ {}, tensor_names, /* targets */ {}, &outputs));
  for (size_t i = 0; i < variable_names.size(); i++) {
    (*variable_name_to_value_map)[variable_names[i]] = outputs[i];
  }
  return OkStatus();
}

// Converts a Variable NodeDef into a Constant NodeDef.

    delete(layout.buildDirectory.file("outputs/1.txt"))
}
// end::removed-output[]

// tag::reverse[]
tasks.register<IncrementalReverseTask>("incrementalReverse") {
    inputDir = file("inputs")
    outputDir = layout.buildDirectory.dir("outputs")
    inputProperty = project.findProperty("taskInputProperty") as String? ?: "original"
}
// end::reverse[]

    private final Compiler<SwiftCompileSpec> compiler;
    private final TaskOutputsInternal outputs;
    private final CompilerOutputFileNamingSchemeFactory compilerOutputFileNamingSchemeFactory;
    private final Deleter deleter;

    public IncrementalSwiftCompiler(
        Compiler<SwiftCompileSpec> compiler,
        TaskOutputsInternal outputs,
        CompilerOutputFileNamingSchemeFactory compilerOutputFileNamingSchemeFactory,

	var (
		gp01      = regInfo{inputs: nil, outputs: []regMask{gp}}
		gp11      = regInfo{inputs: []regMask{gpsp}, outputs: []regMask{gp}}
		gp21      = regInfo{inputs: []regMask{gpsp, gpsp}, outputs: []regMask{gp}}
		gp31      = regInfo{inputs: []regMask{gpsp, gpsp, gpsp}, outputs: []regMask{gp}}
		fp32_01   = regInfo{inputs: nil, outputs: []regMask{fp32}}
		fp32_11   = regInfo{inputs: []regMask{fp32}, outputs: []regMask{fp32}}

        }
      }
      outputs: {
        key: "outputs"
        value: {
          name: "batch_func:0"
        }
      }
    }
  }
  signature_def: {
    key: "predict1"
    value: {
      inputs: {
        key: "tf_example_input"
        value: {
          name: "input0"
          dtype: DT_STRING
        }
      }
      outputs: {
        key: "outputs"

        given:
        def thrownFailure = new RuntimeException('broken')
        action { throw thrownFailure }

        when:
        main.run()

        then:
        outputs.stdErr.contains('FAILURE: Build failed with an exception')
        outputs.stdErr.contains('broken')
        completedWithFailure
        failure == thrownFailure
    }

    def reportsActionCreationFailure() {
        given:

      "execution_count": null,
      "metadata": {
        "id": "RXZUHZQoQZOo"
      },
      "outputs": [],
      "source": [
        "!pip uninstall tensorflow --yes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aYz36YEKPYRk"
      },
      "outputs": [],
      "source": [
        "!pip3 install tf-nightly\n",
        "!pip3 install keras-core"

                @InputArtifact
                abstract Provider<FileSystemLocation> getInput()

                void transform(TransformOutputs outputs) {
                    File inputFile = input.get().asFile
                    println "processing \${inputFile.name}"
                    def output = outputs.file(inputFile.name + "." + parameters.extension)
                    output.text = "ok"
                }
            }
        """