### 2. Code

Provide code to help us reproduce your issues using one of the following options:

#### Option A: Reference colab notebooks

1)  Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.

func (v *Value) InferBytesType() (err error) {
	b, ok := v.ToBytes()
	if !ok {
		return fmt.Errorf("InferByteType: Input is not bytes, but %v", v.GetTypeString())
	}

	// Check for numeric inference
	if x, ok := v.bytesToInt(); ok {
		v.setInt(x)
		return nil
	}
	if x, ok := v.bytesToFloat(); ok {
		v.setFloat(x)
		return nil
	}
	if x, ok := v.bytesToBool(); ok {
		v.setBool(x)

// `shape_inference::InferenceContext` constructor. Note the following:
//   - The inputs of the `op` are not used for shape inference. So, it is
//     OK to not have the inputs properly set in `op`. See `input_tensors`
//     if you want shape inference to consider the input tensors of the
//     op for shape inference.
//   - The types need not be set in `input_shapes` as it is not used.

inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape
inference
functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.

The security impact of skipping those checks should be low, since the attack

“parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and...

   * use the {@code ArrayList} {@linkplain ArrayList#ArrayList() constructor} directly, taking
   * advantage of <a
   * href="https://docs.oracle.com/javase/tutorial/java/generics/genTypeInference.html#type-inference-instantiation">"diamond"
   * syntax</a>.
   */
  @GwtCompatible(serializable = true)
  @SuppressWarnings("NonApiType") // acts as a direct substitute for a constructor call

25786743
```

For a more detailed SELECT SQL reference, please see [here](https://docs.aws.amazon.com/AmazonS3/latest/dev/s3-glacier-select-sql-reference-select.html)

## 5. Explore Further

- [Use `mc` with MinIO Server](https://min.io/docs/minio/linux/reference/minio-mc.html)
- [Use `mc sql` with MinIO Server](https://min.io/docs/minio/linux/reference/minio-mc/mc-sql.html#command-mc.sql)

   * use the {@code HashSet} constructor directly, taking advantage of <a
   * href="https://docs.oracle.com/javase/tutorial/java/generics/genTypeInference.html#type-inference-instantiation">"diamond"
   * syntax</a>.
   */
  @SuppressWarnings("NonApiType") // acts as a direct substitute for a constructor call
  public static <E extends @Nullable Object> HashSet<E> newHashSet() {

struct TF_Library {
  void* lib_handle;
  TF_Buffer op_list;
};

struct TF_Graph {
  TF_Graph();

  mutable tensorflow::mutex mu;
  tensorflow::Graph graph TF_GUARDED_BY(mu);

  // Runs shape inference.
  tensorflow::ShapeRefiner refiner TF_GUARDED_BY(mu);

  // Maps from name of an operation to the Node* in 'graph'.
  std::unordered_map<tensorflow::string, tensorflow::Node*> name_map
      TF_GUARDED_BY(mu);

  }

  ~ShapeInferenceTest() override {
    TFE_DeleteContextOptions(tfe_context_options_);
    TFE_DeleteContext(tfe_context_);
    TF_DeleteStatus(status_);
  }

  // Checks the expected result of shape inference for the given `op`.
  void CheckOutputShapes(
      TFE_Op* op,
      const std::vector<absl::optional<std::vector<int64_t>>>& input_shapes_vec,
      const std::vector<TF_Tensor*>& input_tensors,