* which is necessarily blocking. By implementing an {@code InputStream} it allows consumers to
 * "pull" as much data as they can handle, which is more convenient when dealing with flow
 * controlled, async APIs.
 *
 * @author Chris Nokleberg
 */
@J2ktIncompatible
@GwtIncompatible
@ElementTypesAreNonnullByDefault
final class ReaderInputStream extends InputStream {

         * Future might not even be a ListenableFuture, just a plain Future. That said, similar
         * problems can exist with methods like FutureTask.done(), not to mention slow calls to
         * Thread.interrupt() (as discussed in InterruptibleTask). At the end of the day, it's
         * unlikely that cancel() will be slow, so we can probably get away with calling it while
         * holding a lock. Still, it would be nice to avoid somehow.

//     in `input_shapes`.
//
// The results are returned in `output_shapes` and
// `output_resource_shapes_and_types`. The caller is responsible for freeing the
// memory in these buffers by calling `TF_DeleteShapeAndTypeList`.
TF_CAPI_EXPORT extern void TFE_InferShapes(
    TFE_Op* op, TF_ShapeAndTypeList* input_shapes, TF_Tensor** input_tensors,
    TF_ShapeAndTypeList* input_tensor_as_shapes,

    return getAndUpdate(key, x -> newValue);
  }

  /**
   * Copies all of the mappings from the specified map to this map. The effect of this call is
   * equivalent to that of calling {@code put(k, v)} on this map once for each mapping from key
   * {@code k} to value {@code v} in the specified map. The behavior of this operation is undefined
   * if the specified map is modified while the operation is in progress.

  @CheckForNull
  V put(@ParametricNullness R rowKey, @ParametricNullness C columnKey, @ParametricNullness V value);

  /**
   * Copies all mappings from the specified table to this table. The effect is equivalent to calling
   * {@link #put} with each row key / column key / value mapping in {@code table}.
   *
   * @param table the table to add to this table
   */
  void putAll(Table<? extends R, ? extends C, ? extends V> table);

   * @return an array containing the values of {@code array}, with guaranteed minimum length {@code
   *     minLength}
   */
  public static int[] ensureCapacity(int[] array, int minLength, int padding) {
    checkArgument(minLength >= 0, "Invalid minLength: %s", minLength);
    checkArgument(padding >= 0, "Invalid padding: %s", padding);
    return (array.length < minLength) ? Arrays.copyOf(array, minLength + padding) : array;

totype={constructor:at,init:function(e,t,n,r,i,o){this.elem=e,this.prop=n,this.easing=i||ce.easing._default,this.options=t,this.start=this.now=this.cur(),this.end=r,this.unit=o||(ce.cssNumber[n]?"":"px")},cur:function(){var e=at.propHooks[this.prop];return e&&e.get?e.get(this):at.propHooks._default.get(this)},run:function(e){var t,n=at.propHooks[this.prop];return this.options.duration?this.pos=t=ce.easing[this.easing](e,this.options.duration*e,0,1,this.options.duration):this.pos=t=e,this.now=(th...

        .snapshot();
  }

  /**
   * Returns a {@link Collector} which accumulates statistics from a {@link java.util.stream.Stream}
   * of any type of boxed {@link Number} into a {@link Stats}. Use by calling {@code
   * boxedNumericStream.collect(toStats())}. The numbers will be converted to {@code double} values
   * (which may cause loss of precision).
   *

   * @return an array containing the values of {@code array}, with guaranteed minimum length {@code
   *     minLength}
   */
  public static char[] ensureCapacity(char[] array, int minLength, int padding) {
    checkArgument(minLength >= 0, "Invalid minLength: %s", minLength);
    checkArgument(padding >= 0, "Invalid padding: %s", padding);

#include "tensorflow/core/platform/logging.h"

namespace tensorflow {

namespace {

// Managing context for the DLManagedTensor, will manage the lifetime of
// DLManagedTensor. When calling DLManagedTensor::deleter, it will notify the
// original framework of destruction, and this context will be deleted also.
struct TfDlManagedTensorCtx {
  TensorReference reference;
  std::vector<int64_t> shape;