--input $(location :tensorflow_def_file) \\
              --output $@
          """,
        "//conditions:default": "touch $@",  # Just a placeholder for Unix platforms
    }),
    tools = ["@local_config_def_file_filter//:def_file_filter"],
    visibility = ["//visibility:public"],
)

    /**
     * Timeout in milliseconds for thumbnail task queue operations.
     */
    protected long thumbnailTaskQueueTimeout = 10 * 1000L;

    /**
     * Expiration time in milliseconds for no-image placeholder files.
     */
    protected long noImageExpired = 24 * 60 * 60 * 1000L; // 24 hours

    /**
     * Hash size for splitting thumbnail storage directories.
     */
    protected int splitHashSize = 10;

// `tensor_id`, associated with `session`. There must be a graph node named
// "fifo_queue_enqueue_<tensor_id>", to be executed by this API call. It reads
// from a placeholder node "arg_tensor_enqueue_<tensor_id>".
//
// `tensor` is still owned by the caller. This call will be blocked if the queue
// has reached its capacity, and will be unblocked when the queued tensors again

        relatedContentHelper.setQueryPlaceHolder("{{QUERY}}");
        assertEquals("{{QUERY}}", relatedContentHelper.queryPlaceHolder);

        // Test with the new placeholder
        List<RelatedContent> testData = new ArrayList<>();
        testData.add(createRelatedContent("regex:test.*", "Content for {{QUERY}}", ""));
        mockBhv.setTestData(testData);

            while (handle.isActive()) {
                try {
                    // Note: Actual SMB2 Change Notify implementation would go here
                    // For now, this is a placeholder that simulates monitoring

                    // In a real implementation, we would:
                    // 1. Send SMB2 Change Notify request
                    // 2. Wait for response

				<div class="input-group">
					<input class="form-control form-control-sidebar" name="q" id="query"
						   maxlength="1000"
						   placeholder="<la:message key="labels.sidebar.placeholder_search" />">
					<div class="input-group-append">
						<button class="btn btn-sidebar" type="submit" name="search" id="search-btn">
							<em class="fa fa-search">

   *     message is formed by replacing each {@code %s} placeholder in the template with an
   *     argument. These are matched by position - the first {@code %s} gets {@code
   *     errorMessageArgs[0]}, etc. Unmatched arguments will be appended to the formatted message in
   *     square braces. Unmatched placeholders will be left as-is.

 {\n  .placeholder {\n    animation: placeholder-glow 2s ease-in-out infinite;\n  }\n}\n\n@keyframes placeholder-glow {\n  50% {\n    opacity: $placeholder-opacity-min;\n  }\n}\n\n.placeholder-wave {\n  mask-image: linear-gradient(130deg, $black 55%, rgba(0, 0, 0, (1 - $placeholder-opacity-min)) 75%, $black 95%);\n  mask-size: 200% 100%;\n  animation: placeholder-wave 2s linear infinite;\n}\n\n@keyframes placeholder-wave {\n  100% {\n    mask-position: -200% 0%;\n  }\n}\n","// All-caps `RGBA()` function...

}

void TF_SetAttrPlaceholder(TF_OperationDescription* desc, const char* attr_name,
                           const char* placeholder) {
  tensorflow::AttrValue attr_value;
  attr_value.set_placeholder(placeholder);
  desc->node_builder.Attr(attr_name, attr_value);
}

void TF_SetAttrFuncName(TF_OperationDescription* desc, const char* attr_name,

      session->graph,
      tensorflow::strings::StrCat("arg_tensor_enqueue_", tensor_id).c_str());
  if (placeholder_op == nullptr) {
    status->status = tensorflow::errors::Internal(
        "Unable to find the placeholder node as input to enqueue in the TF "
        "graph.");
    return;
  }

  VLOG(1) << "Running the enqueue op";
  TF_Output input{placeholder_op, 0};
  TF_SessionRun(session, /*run_options*/ nullptr,