// allocated to size `ninputs`. The caller should build `cond_graph` and
// `body_graph` starting from the inputs, and store the final outputs in
// `cond_output` and `body_outputs`.
//
// If `status` is OK, the caller must call either TF_FinishWhile or
// TF_AbortWhile on the returned TF_WhileParams. If `status` isn't OK, the
// returned TF_WhileParams is not valid, and the caller should not call

#include "tensorflow/core/protobuf/tensorflow_server.pb.h"

// Return a tensor handle containing a float scalar
TFE_TensorHandle* TestScalarTensorHandle(TFE_Context* ctx, float value);

// Return a tensor handle containing a int scalar
TFE_TensorHandle* TestScalarTensorHandle(TFE_Context* ctx, int value);

// Return a tensor handle containing a bool scalar
TFE_TensorHandle* TestScalarTensorHandle(TFE_Context* ctx, bool value);

MinIO is a high performance distributed object storage server, designed for large-scale private cloud infrastructure. MinIO is designed in a cloud-native manner to scale sustainably in multi-tenant environments. Orchestration platforms like Kubernetes provide perfect cloud-native environment to deploy and scale MinIO.

## MinIO Deployment on Kubernetes

There are multiple options to deploy MinIO on Kubernetes:

TF_Operation* ScalarConst(bool v, TF_Graph* graph, TF_Status* s,
                          const char* name = "scalar");

TF_Operation* ScalarConst(int32_t v, TF_Graph* graph, TF_Status* s,
                          const char* name = "scalar");

TF_Operation* ScalarConst(double v, TF_Graph* graph, TF_Status* s,
                          const char* name = "scalar");

TF_Operation* ScalarConst(float v, TF_Graph* graph, TF_Status* s,

     */
    CLUSTER_WITNESS,

    /**
     * Individual file server witness for standalone servers
     */
    FILE_SERVER_WITNESS,

    /**
     * Scale-out file server witness for scale-out deployments
     */
    SCALE_OUT_WITNESS,

    /**
     * DFS namespace witness for distributed file system resources
     */
    DFS_WITNESS

  int num_dims_out = TF_NumDims(model_out_tensor);
  TF_DeleteTensor(model_out_tensor);

  // If the output is a scalar, then return the scalar output
  if (num_dims_out == 0) {
    outputs[0] = model_out.release();
    return absl::OkStatus();
  }

  // Else, reduce sum the output to get a scalar

  // Will sum all dimensions, so get a Tensor containing [0,...,num_dims_out-1].
  AbstractTensorHandlePtr sum_dims;
  {

### Creating the Scale-out Backup Repository

- Under the Backup Infrastructure view, click on Scale-out Repositories and click the Add Scale-out Repository button on the ribbon.

- Follow the on screen wizard

    // Add small multiples of MIN_VALUE and MIN_NORMAL
    for (int scale = 1; scale <= 4; scale++) {
      for (double d : Doubles.asList(Double.MIN_VALUE, Double.MIN_NORMAL)) {
        fractionalBuilder.add(d * scale).add(-d * scale);
      }
    }
    for (int i = Double.MIN_EXPONENT; i <= Double.MAX_EXPONENT; i++) {
      for (int direction : new int[] {1, -1}) {

/**
 * Static utility methods pertaining to {@link Escaper} instances.
 *
 * @author Sven Mawson
 * @author David Beaumont
 * @since 15.0
 */
@GwtCompatible
public final class Escapers {
  private Escapers() {}

  /**
   * Returns an {@link Escaper} that does no escaping, passing all character data through unchanged.
   */
  public static Escaper nullEscaper() {
    return NULL_ESCAPER;
  }

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