limitations under the License.
==============================================================================*/

#include "tensorflow/c/eager/c_api_unified_experimental.h"

#include <vector>

#include "absl/container/flat_hash_map.h"
#include "absl/strings/str_cat.h"
#include "tensorflow/c/eager/c_api_unified_experimental_internal.h"
#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/c/tf_status.h"

  // REMOTE, REMOTE, LOCAL on task0. When remote is true, function device is
  // placed on task1, Handle types are LOCAL, REMOTE, LOCAL on task1.
  int num_replicas = 3;
  std::vector<TFE_TensorHandle*> handles = {h0, h1, h2};
  TFE_TensorHandle* packed_handle =
      TFE_CreatePackedTensorHandle(ctx, handles.data(), &num_replicas, status);
  ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);

  // `ctx_0`, `ctx_1`, `ctx_2` contains `remote_device`.
  {
    const std::vector<std::string>& device_names = ListDeviceNames(ctx_0);
    ASSERT_TRUE(std::find(device_names.begin(), device_names.end(),
                          remote_device) != device_names.end());
  }

  {
    const std::vector<std::string>& device_names = ListDeviceNames(ctx_1);

</p>

<p>
If a vector instruction takes a length or an index as an argument then it will be the
first argument.
For example, <code>VLEIF</code> <code>$1,</code> <code>$16,</code> <code>V2</code> will load
the value sixteen into index one of <code>V2</code>.
Care should be taken when using vector instructions to ensure that they are available at
runtime.

        assertSame(loc, res);
        // No connect attempts expected
        verify(c, never()).connectWrapException(any());
    }

    @Test
    @DisplayName("send retries on transport errors and restores request/response state")
    void send_retries_on_transportError() throws Exception {
        // Create a spy to track method calls
        SmbTreeConnection c = spy(newConn());

        // Prepare a minimal locator

        byte[] buffer = new byte[22];
        int idFileSystem = 0x22222222;
        int sectPerAlloc = 8;
        long alloc = 10000L;
        long free = 5000L;
        int bytesPerSect = 32768; // 32KB sectors

        // Encode test data
        int offset = 0;
        SMBUtil.writeInt4(idFileSystem, buffer, offset);
        offset += 4;
        SMBUtil.writeInt4(sectPerAlloc, buffer, offset);
        offset += 4;

            searchPaging(data, form);
        });
    }

    /**
     * Populate render data with stemmer override items for pagination display.
     * Retrieves items based on search criteria and restores form data from pager.
     *
     * @param data The render data to populate
     * @param form The search form containing criteria
     */
    protected void searchPaging(final RenderData data, final SearchForm form) {

            searchPaging(data, form);
        });
    }

    /**
     * Sets up pagination data for the web config search results.
     * Registers web config items and restores search criteria from the pager.
     *
     * @param data the render data to populate with search results
     * @param form the search form containing filter criteria
     */

			}
		}
	} else if err := delServerConfigHistory(ctx, objectAPI, restoreID); err != nil {
		writeErrorResponseJSON(ctx, w, toAdminAPIErr(ctx, err), r.URL)
		return
	}
}

// RestoreConfigHistoryKVHandler - restores a config with KV settings for the given KV id.
func (a adminAPIHandlers) RestoreConfigHistoryKVHandler(w http.ResponseWriter, r *http.Request) {
	ctx := r.Context()

  TF_AbstractOpSetOpType(fn_op, fn_name.c_str(), s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Build two abstract input tensors as function arguments.
  std::vector<TF_AbstractTensor*> func_args;
  {
    TFE_Context* eager_ctx =
        TF_ExecutionContextGetTFEContext(eager_execution_ctx, status.get());
    ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());