result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

        "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.

* Each application that you have running on your computer has some process behind it, each running program, each window, etc. And there are normally many processes running **at the same time** while a computer is on.
* There can be **multiple processes** of the **same program** running at the same time.

  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);

        // use original key
        return parent.computeIfPresent(key, remappingFunction);
    }

    @Override
    public V compute(final K key, final BiFunction<? super K, ? super V, ? extends V> remappingFunction) {
        // use original key
        return parent.compute(key, remappingFunction);
    }

    @Override

	if !ok {
		return nil
	}
	return &due
}

// isCompacted returns whether an entry is compacted.
// Returns false if not found.
func (d *dataUsageCache) isCompacted(h dataUsageHash) bool {
	due, ok := d.Cache[h.Key()]
	if !ok {
		return false
	}
	return due.Compacted
}

// findChildrenCopy returns a copy of the children of the supplied hash.

     */
    @Nullable
    <T> T get(@Nonnull Key<T> key);

    /**
     * Retrieve of compute the data associated with the specified key.
     *
     * @param key the key for which to retrieve the session data, must not be {@code null}
     * @param supplier the supplier will compute the new value
     * @return the session data associated with the key
     */
    @Nullable