This way, its schema will show up in the API docs user interface:

<img src="/img/tutorial/dataclasses/image01.png">

## Dataclasses in Nested Data Structures

You can also combine `dataclasses` with other type annotations to make nested data structures.

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

- **Flexibility and Control:**
Java serialization offers limited control over the serialization process, such as excluding certain fields, customizing naming conventions, and handling complex data structures more gracefully.

- **Security:**
Java serialization poses security risks, especially related to deserialization vulnerabilities.

- **Version Compatibility:**

  // Get theta shape and store in theta_dims.
  int num_dims = TF_NumDims(theta_tensor);
  vector<int64_t> theta_dims(num_dims);
  GetDims(theta_tensor, theta_dims.data());

  // Initialize auxilary data structures.
  vector<float> thetaPlus_data(num_elems);
  vector<float> thetaMinus_data(num_elems);
  AbstractTensorHandle* f_outputs[1];

  // Numerical Grad Check
  for (int i = 0; i < num_elems; i++) {

    return set;
  }

  /**
   * Creates a {@code CompactLinkedHashSet} instance, with a high enough "initial capacity" that it
   * <i>should</i> hold {@code expectedSize} elements without rebuilding internal data structures.
   *
   * @param expectedSize the number of elements you expect to add to the returned set
   * @return a new, empty {@code CompactLinkedHashSet} with enough capacity to hold {@code

  }

  /**
   * Okio buffers are internally implemented as a linked list of arrays. Usually this implementation
   * detail is invisible to the caller, but subtle use of certain APIs may depend on these internal
   * structures.
   *
   * We make such subtle calls in [okhttp3.internal.ws.MessageInflater] because we try to read a
   * compressed stream that is terminated in a web socket frame even though the DEFLATE stream is
   * not terminated.

    * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:
    * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.

  }

  /**
   * Creates a {@code CompactLinkedHashMap} instance, with a high enough "initial capacity" that it
   * <i>should</i> hold {@code expectedSize} elements without rebuilding internal data structures.
   *
   * @param expectedSize the number of elements you expect to add to the returned set
   * @return a new, empty {@code CompactLinkedHashMap} with enough capacity to hold {@code

    return set;
  }

  /**
   * Creates a {@code CompactLinkedHashSet} instance, with a high enough "initial capacity" that it
   * <i>should</i> hold {@code expectedSize} elements without rebuilding internal data structures.
   *
   * @param expectedSize the number of elements you expect to add to the returned set
   * @return a new, empty {@code CompactLinkedHashSet} with enough capacity to hold {@code

	return signedHeaders, ErrNone
}

// signValues data type represents structured form of AWS Signature V4 header.
type signValues struct {
	Credential    credentialHeader
	SignedHeaders []string
	Signature     string
}

// preSignValues data type represents structured form of AWS Signature V4 query string.
type preSignValues struct {
	signValues
	Date    time.Time

# Tutorial - User Guide

This tutorial shows you how to use **FastAPI** with most of its features, step by step.

Each section gradually builds on the previous ones, but it's structured to separate topics, so that you can go directly to any specific one to solve your specific API needs.

It is also built to work as a future reference.

So you can come back and see exactly what you need.

## Run the code