}
            );

            this.resize();
            suggestor = this;
            $(window).resize(function() {
              suggestor.resize();
            });

            $("body").append($boxElement);
          },

          suggest: function() {
            suggestingSts = true;

            this.resize();

            var suggestor = this;

    if (read >= 0) content.resize(read);
    if (file_size != content.size())
      TF_SetStatus(
          status_, TF_DATA_LOSS,
          std::string("expected " + std::to_string(file_size) + " got " +
                      std::to_string(content.size()) + " bytes")
              .c_str());
    return content;
  }

 protected:
  TF_Filesystem* filesystem_;
  TF_Status* status_;

 private:

      newDelegate.addAll(delegate);
      this.table = newDelegate;
      return;
    }
    int size = this.size;
    if (size < requireEntries().length) {
      resizeEntries(size);
    }
    int minimumTableSize = CompactHashing.tableSize(size);
    int mask = hashTableMask();
    if (minimumTableSize < mask) { // smaller table size will always be less than current mask

      newDelegate.addAll(delegate);
      this.table = newDelegate;
      return;
    }
    int size = this.size;
    if (size < requireEntries().length) {
      resizeEntries(size);
    }
    int minimumTableSize = CompactHashing.tableSize(size);
    int mask = hashTableMask();
    if (minimumTableSize < mask) { // smaller table size will always be less than current mask

  std::vector<int64_t>* shape_arr = &tf_dlm_tensor_ctx->shape;
  std::vector<int64_t>* stride_arr = &tf_dlm_tensor_ctx->strides;
  shape_arr->resize(ndim);
  stride_arr->resize(ndim, 1);
  for (int i = 0; i < ndim; i++) {
    (*shape_arr)[i] = tensor->dim_size(i);
  }
  for (int i = ndim - 2; i >= 0; --i) {
    (*stride_arr)[i] = (*shape_arr)[i + 1] * (*stride_arr)[i + 1];
  }

  outputs_.clear();
  for (TF_Operation* o : outputs) {
    outputs_.emplace_back(TF_Output{o, 0});
  }
  output_values_.resize(outputs_.size());
}

void CSession::SetOutputs(const std::vector<TF_Output>& outputs) {
  ResetOutputValues();
  outputs_ = outputs;
  output_values_.resize(outputs_.size());
}

void CSession::SetTargets(std::initializer_list<TF_Operation*> targets) {
  targets_.clear();

  size_t start = block_size_ * (offset / block_size_);
  size_t finish = block_size_ * ((offset + n) / block_size_);
  if (finish < offset + n) {
    finish += block_size_;
  }
  size_t total_bytes_transferred = 0;
  // Now iterate through the blocks, reading them one at a time.
  for (size_t pos = start; pos < finish; pos += block_size_) {
    Key key = std::make_pair(filename, pos);

set FESS_MIN_MEM=256m
)

if "%FESS_MAX_MEM%" == "" (
set FESS_MAX_MEM=1g
)

if NOT "%FESS_HEAP_SIZE%" == "" (
set FESS_MIN_MEM=%FESS_HEAP_SIZE%
set FESS_MAX_MEM=%FESS_HEAP_SIZE%
)

REM min and max heap sizes should be set to the same value to avoid
REM stop-the-world GC pauses during resize, and so that we can lock the
REM heap in memory on startup to prevent any of it from being swapped
REM out.

    }
    int size = this.size;
    if (size < requireEntries().length) {
      resizeEntries(size);
    }
    int minimumTableSize = CompactHashing.tableSize(size);
    int mask = hashTableMask();
    if (minimumTableSize < mask) { // smaller table size will always be less than current mask
      resizeTable(mask, minimumTableSize, UNSET, UNSET);
    }
  }

  @Override
  public void clear() {

fi

if [ "x$FESS_MIN_MEM" = "x" ]; then
    FESS_MIN_MEM=256m
fi
if [ "x$FESS_MAX_MEM" = "x" ]; then
    FESS_MAX_MEM=2g
fi
if [ "x$FESS_HEAP_SIZE" != "x" ]; then
    FESS_MIN_MEM=$FESS_HEAP_SIZE
    FESS_MAX_MEM=$FESS_HEAP_SIZE
fi

# External opensearch cluster
#SEARCH_ENGINE_HTTP_URL=http://localhost:9200
#FESS_DICTIONARY_PATH=/var/lib/opensearch/config/