[[sec:task_output_caching_details]]
== Cacheable tasks

Since a task describes all of its inputs and outputs, Gradle can compute a _build cache key_ that uniquely defines the task's outputs based on its inputs.
That build cache key is used to request previous outputs from a build cache or store new outputs in the build cache.

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

== 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.

            withArgument("--max-workers=$threadCount")
        }
    }

    def "overlapping outputs prevent parallel execution"() {
        given:
        withParallelThreads(2)

        and:
        buildFile << """
            aPing.outputs.dir "dir"
            bPing.outputs.file "dir/file"
        """
        expect:
        2.times {

	k = len(i.outputs)
	for _, p := range a.abiInfo.OutParams() {
		for _, r := range p.Registers {
			m := archRegForAbiReg(r, c)
			a.reg.outputs = append(a.reg.outputs, outputInfo{idx: k, regs: (1 << m)})
			k++
		}
	}
	a.reg.outputs = append(a.reg.outputs, i.outputs...)
	a.reg.clobbers = i.clobbers
	return a.reg
}

    std::cout << '\'' << subgraph_name << "' outputs:\n";
    mlir::Operation *terminator = nullptr;
    if (subgraph->name()) {
      if (auto fn = module.lookupSymbol<FuncOp>(subgraph->name()->str()))
        terminator = fn.back().getTerminator();
    }
    i = 0;
    for (auto output : *subgraph->outputs()) {
      print_buffer(*subgraph, i, output, [&](int i) {

  return absl::OkStatus();
}

// Converts `noutputs` and `outputs` into `outputs_tensors` and does various
// checks while doing so.
Status ProcessOutputs(const TF_Graph* fn_body, const char* fn_name,
                      int noutputs, const TF_Output* outputs,
                      std::vector<OutputTensor>* output_tensors)
    TF_EXCLUSIVE_LOCKS_REQUIRED(fn_body->mu) {
  output_tensors->reserve(noutputs);
  for (int i = 0; i < noutputs; ++i) {

        absl::StrCat("ZerosLike", ToId(t)).c_str()));
  }
  TF_RETURN_IF_ERROR(op->AddInput(t));
  int num_outputs = 1;
  std::vector<AbstractTensorHandle*> outputs(num_outputs);
  TF_RETURN_IF_ERROR(
      op->Execute(absl::Span<AbstractTensorHandle*>(outputs), &num_outputs));
  *result = outputs[0];
  return absl::OkStatus();
}
}  // namespace

Status GradientRegistry::Register(

  }
  return absl::OkStatus();
}

Status MlirFunctionContext::Finalize(OutputList* outputs,
                                     AbstractFunction** f) {
  Block& body = func_.getBody().front();
  SmallVector<Value, 8> ret_operands;
  for (auto* output : outputs->outputs) {
    auto* operand = dyn_cast<MlirTensor>(output);
    if (!operand)
      return InvalidArgument("Capturing eager tensors is not supported yet.");

                @javax.inject.Inject
                abstract FileSystemOperations getFileSystemOperations()

                @Override
                void transform(TransformOutputs outputs) {
                    File augmentedDir = outputs.dir("augmented")

                    System.out.println "Augmenting..."
                    fileSystemOperations.copy {
                        it.from input

          "should have been identified by this point.");
    }

    shardings.push_back(std::move(sharding.value()));
  }

  return absl::OkStatus();
}

// Determines XlaSharding for inputs and outputs. If there are aliased
// inputs/outputs for which no sharding was found directly, the corresponding