* 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.

  // 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++) {

  }

  /**
   * 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.

		l.LastSec++
	}
}

// LastMinuteHistogram keeps track of last minute sizes added.
type LastMinuteHistogram [sizeLastElemMarker]lastMinuteLatency

// Merge safely merges two LastMinuteHistogram structures into one
func (l LastMinuteHistogram) Merge(o LastMinuteHistogram) (merged LastMinuteHistogram) {
	for i := range l {
		merged[i] = l[i].merge(o[i])
	}
	return merged
}

  }

  /**
   * 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

// op executed under the tape. A separate map (`tensorflow::eager::OpTape`)
// maintains the map from `op_id` to a `OpTapeEntry` which stores the `op_type`,
// inputs and outputs and the gradient function These data structures combined
// allow us to trace the data dependencies between operations and hence compute
// gradients.
//
// `ZerosLike` is not expected to be called and returns a nullptr. The creation

#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/public/session.h"

namespace tensorflow {
class Device;
class DeviceMgr;
class ServerInterface;
}  // namespace tensorflow

// Internal structures used by the C API. These are likely to change and should
// not be depended on.

struct TF_SessionOptions {
  tensorflow::SessionOptions options;
};

struct TF_DeprecatedSession {
  tensorflow::Session* session;

  }

  /**
   * 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

* The PR doesn't add any additional or extraneous files.
* The translation seems to have a similar structure as the original English file.
* The translation doesn't seem to change the original content, for example with obvious additional documentation sections.
* The translation doesn't use different Markdown structures, for example adding HTML tags when the original didn't have them.