absl::Span<AbstractTensorHandle*> outputs) {
  Tape tape(/*persistent=*/false);
  tape.Watch(inputs[0]);
  AbstractTensorHandle* neg_output;
  TF_RETURN_IF_ERROR(ops::Neg(ctx, inputs[0], &neg_output, "Neg"));
  tape.RecordOperation(inputs, {neg_output}, nullptr, "Neg");
  return tape.ComputeGradient(ctx,
                              /*targets=*/{neg_output},
                              /*sources=*/inputs,

            CACHEBUSTER=${{ steps.tf-version.outputs.DATE }}
          tags: |
            tensorflow/build:latest-${{ matrix.python-version }}
            tensorflow/build:${{ steps.tf-version.outputs.TF_VERSION }}-${{ matrix.python-version }}
            gcr.io/tensorflow-sigs/build:latest-${{ matrix.python-version }}
            gcr.io/tensorflow-sigs/build:${{ steps.tf-version.outputs.TF_VERSION }}-${{ matrix.python-version }}

            CACHEBUSTER=${{ steps.vars.outputs.DATE }}
          tags: |
            tensorflow/build:${{ steps.vars.outputs.REF }}-${{ matrix.python-version }}
            gcr.io/tensorflow-sigs/build:${{ steps.vars.outputs.REF }}-${{ matrix.python-version }}
          cache-from: type=registry,ref=tensorflow/build:${{ steps.vars.outputs.REF }}-${{ matrix.python-version }}
          cache-to: type=inline
      -

  sources.reserve(output_tensors.size());
  for (const TapeTensor& output_tensor : output_tensors) {
    // Ownership of `aid` transferred to CallBackwardFunction below.
    Gradient* aid;
    if (output_tensor.GetDType() == tensorflow::DT_VARIANT) {
      // Note: Needs to be zeros rather than ones since there's currently no
      // ones_like for variants.
      aid = output_tensor.ZerosLike();
    } else {

    if (TF_GetCode(status) != TF_OK) return nullptr;
    TFE_OpSetAttrType(const_op.get(), "dtype", datatype_enum);
    TFE_TensorHandle* device_handle;
    int num_outputs = 1;
    TFE_Execute(const_op.get(), &device_handle, &num_outputs, status);
    if (TF_GetCode(status) != TF_OK) return nullptr;
    components.emplace_back(device_handle);
  }
  return ParallelTensor::FromTensorHandles(*this, std::move(components),

Xiaopeng Han <******@****.***> 1682086643 +0800

zirain <******@****.***> 1657592759 +0800

        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(

        requestBody.put(KEY_PROPERTY, keyProp);
        requestBody.put("outputs", "output");
        return requestBody;
    }

    @Override
    protected Map<String, Object> getUpdateMap() {
        final Map<String, Object> updateMap = new HashMap<>();
        updateMap.put("outputs", "new_outputs");
        return updateMap;
    }

    @Test
    void crudTest() {
        testCreate();

      - name: multiple pools
        run: |
          ${GITHUB_WORKSPACE}/.github/workflows/run-mint.sh "pools" "minio" "minio123" "${{ steps.vars.outputs.sha_short }}"

      - name: standalone erasure
        run: |
          ${GITHUB_WORKSPACE}/.github/workflows/run-mint.sh "erasure" "minio" "minio123" "${{ steps.vars.outputs.sha_short }}"