}

  public void testScale_index_compute_doubleCollection() {
    assertThat(Quantiles.scale(10).index(1).compute(SIXTEEN_SQUARES_DOUBLES))
        .isWithin(ALLOWED_ERROR)
        .of(SIXTEEN_SQUARES_DECILE_1);
  }

  public void testScale_index_compute_longCollection() {
    assertThat(Quantiles.scale(10).index(1).compute(SIXTEEN_SQUARES_LONGS))
        .isWithin(ALLOWED_ERROR)

  }

  public void testScale_index_compute_doubleCollection() {
    assertThat(Quantiles.scale(10).index(1).compute(SIXTEEN_SQUARES_DOUBLES))
        .isWithin(ALLOWED_ERROR)
        .of(SIXTEEN_SQUARES_DECILE_1);
  }

  public void testScale_index_compute_longCollection() {
    assertThat(Quantiles.scale(10).index(1).compute(SIXTEEN_SQUARES_LONGS))
        .isWithin(ALLOWED_ERROR)

 *
 * <h3>Examples</h3>
 *
 * <p>To compute the median:
 *
 * <pre>{@code
 * double myMedian = median().compute(myDataset);
 * }</pre>
 *
 * where {@link #median()} has been statically imported.
 *
 * <p>To compute the 99th percentile:
 *
 * <pre>{@code
 * double myPercentile99 = percentiles().index(99).compute(myDataset);
 * }</pre>
 *

    native_cuda_compute_capabilities = get_native_cuda_compute_capabilities(
        environ_cp)
    if not native_cuda_compute_capabilities:
      default_cuda_compute_capabilities = _DEFAULT_CUDA_COMPUTE_CAPABILITIES
    else:
      default_cuda_compute_capabilities = native_cuda_compute_capabilities

    ask_cuda_compute_capabilities = (
        'Please specify a list of comma-separated CUDA compute capabilities '

                    },
                    "summary": "Compute",
                    "operationId": "compute_a_compute_post",
                    "requestBody": {
                        "content": {
                            "application/json": {
                                "schema": {
                                    "$ref": "#/components/schemas/Body_compute_a_compute_post"
                                }

from fastapi import APIRouter, Body

router = APIRouter()


@router.post("/compute")
def compute(a: int = Body(), b: str = Body()):

    // b/80241237

    Cache<String, String> c = CacheBuilder.newBuilder().maximumSize(1).build();

    assertThat(c.asMap().compute("hash-1", (k, v) -> "a")).isEqualTo("a");
    assertThat(c.asMap().compute("hash-1", (k, v) -> "b")).isEqualTo("b");
    assertThat(c.asMap().compute("hash-1", (k, v) -> "c")).isEqualTo("c");
    assertThat(c.size()).isEqualTo(1);
    assertThat(c.asMap().computeIfAbsent("hash-2", k -> "")).isEqualTo("");

        "Expected numeric or variant tensor, got dtype ", dtype);
  }
  return tensorflow::ops::Conj(ctx, input, output, name);
}

class AddGradientFunction : public GradientFunction {
 public:
  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    // TODO(b/161805092): Support broadcasting.

  TF_Status* s = TF_NewStatus();
  TF_OpKernelConstruction_GetAttrType(ctx, "foobar", &type, s);
  TF_DeleteStatus(s);
  return NULL;
}

// A compute function. This will never actually get called in this test, it's
// just nice to know that it compiles.
void compute(void* kernel, TF_OpKernelContext* ctx) {
  TF_Tensor* input;
  TF_Status* s = TF_NewStatus();
  TF_GetInput(ctx, 0, &input, s);
  TF_DeleteTensor(input);

    // Compute the filter.
    long filter = 0;
    int size = chars.cardinality();
    boolean containsZero = chars.get(0);
    // Compute the hash table.
    char[] table = new char[chooseTableSize(size)];
    int mask = table.length - 1;
    for (int c = chars.nextSetBit(0); c != -1; c = chars.nextSetBit(c + 1)) {
      // Compute the filter at the same time.
      filter |= 1L << c;