Add the following optimal entries for `hive-site.xml` to configure Hive with **MinIO**.

```
hive.blobstore.use.blobstore.as.scratchdir=true
hive.exec.input.listing.max.threads=50
hive.load.dynamic.partitions.thread=25
hive.metastore.fshandler.threads=50
hive.mv.files.threads=40
mapreduce.input.fileinputformat.list-status.num-threads=50
```

        }
    }

    public void update( byte[] input, int offset, int len ) {
        if( log.level >= 5 ) {
            log.println( "update: " + updates + " " + offset + ":" + len );
            Hexdump.hexdump( log, input, offset, Math.min( len, 256 ));
            log.flush();
        }
        if( len == 0 ) {
            return; /* CRITICAL */
        }
        digest.update( input, offset, len );
        updates++;
    }

TF_Operation* RandomUniform(TF_Operation* shape, TF_DataType dtype,
                            TF_Graph* graph, TF_Status* s);

// Split `input` along the first dimension into 3 tensors
TF_Operation* Split3(TF_Operation* input, TF_Graph* graph, TF_Status* s,
                     const char* name = "split3");

bool IsPlaceholder(const tensorflow::NodeDef& node_def);

   * (NCR). Any non-ASCII characters appearing in the input will be preserved in the output.
   * Specifically "\r" (carriage return) is preserved in the output, which may result in it being
   * silently converted to "\n" when the XML is parsed.
   *
   * <p>This escaper does not treat surrogate pairs specially and does not perform Unicode
   * validation on its input.
   */
  public static Escaper xmlContentEscaper() {

    var delta = 0
    var bias = INITIAL_BIAS
    var h = b
    while (h < input.size) {
      val m = input.minBy { if (it >= n) it else Int.MAX_VALUE }

      val increment = (m - n) * (h + 1)
      if (delta > Int.MAX_VALUE - increment) return false // Prevent overflow.
      delta += increment

      n = m

      for (c in input) {
        if (c < n) {

    device_name_ = name;
    return absl::OkStatus();
  }

  absl::Status AddInput(AbstractTensorHandle* input) override {
    GraphTensor* t = dyn_cast<GraphTensor>(input);
    if (!t) {
      return tensorflow::errors::InvalidArgument(
          "Unable to cast input to GraphTensor");
    }
    TF_AddInput(op_.get(), t->output_);
    return absl::OkStatus();
  }
  absl::Status AddInputList(

    assertFailsToDecode(base64(), "12345", "Invalid input length 5");
    // These have a combination of invalid length, unrecognized characters and wrong padding.
    assertFailsToDecode(base64(), "AB=C", "Unrecognized character: =");
    assertFailsToDecode(base64(), "A=BCD", "Invalid input length 5");
    assertFailsToDecode(base64(), "?", "Invalid input length 1");
  }

  public void testBase64CannotUpperCase() {

  @Override
  protected String getSuccessfulResult() {
    return RESULT_DATA;
  }

  private class ComposeFunction implements Function<Integer, String> {
    @Override
    public String apply(Integer input) {
      if (input.intValue() == VALID_INPUT_DATA) {
        return RESULT_DATA;
      } else {
        throw WRAPPED_EXCEPTION;
      }
    }
  }

  public void testFutureGetThrowsFunctionException() throws Exception {

   * Returns an output {@code Future} to use in place of the given {@code input}. The output {@code
   * Future} need not be {@linkplain Future#isDone done}, making {@code AsyncFunction} suitable for
   * asynchronous derivations.
   *
   * <p>Throwing an exception from this method is equivalent to returning a failing {@code Future}.
   */
  ListenableFuture<O> apply(@ParametricNullness I input) throws Exception;

@Experimental
public interface ModelTransformer {

    /**
     * Apply a transformation on the file model.
     *
     * @param model the input model
     * @param problems the problem collector to report any issues encountered during transformation
     * @return the transformed model, or the input model if no transformation is needed
     */
    @Nonnull
    Model transform(