//   1. DENSE: each coordinate in this dimension is stored implicitly.
//   2. SPARSE_CSR: only the coordinates with non-zero elements are stored. The
//      compression technique is the same what CSR uses.
// More types like a sparse dimension with a different compression technique
// could be added to the list in the future.
enum DimensionType : byte {
  DENSE = 0,
  SPARSE_CSR = 1,
}

table Int32Vector {
  values:[int];
}

    ] = None,
) -> autotrackable.AutoTrackable:
  """Quantizes the given SavedModel via static range quantization.

  If the model is not trained with Quantization-Aware Training (QAT) technique,
  it requires `representative_dataset` to collect statistics required for
  quantization. If non-None `representative_dataset` is provided with a QAT
  model input, `representative_dataset` will be ignored.

  Args:

	// operator deletes a suggested configuration object and later creates it
	// but with a variant spec and then does no updates of the object
	// (generation is 1), the technique outlined above will incorrectly
	// determine that the object should be auto-updated.
	AutoUpdateAnnotationKey = "apf.kubernetes.io/autoupdate-spec"
)

// +genclient
// +genclient:nonNamespaced

See the documentation for <<cross_project_publications.adoc#cross_project_publications, sharing outputs between projects>> for more information.

	tx = db.getInstance()
	tx.Statement.Model = value
	return
}

// Clauses Add clauses
//
// This supports both standard clauses (clause.OrderBy, clause.Limit, clause.Where) and more
// advanced techniques like specifying lock strength and optimizer hints. See the
// [docs] for more depth.
//
//	// add a simple limit clause
//	db.Clauses(clause.Limit{Limit: 1}).Find(&User{})

* Security and authentication, including support for **OAuth2** with **JWT tokens** and **HTTP Basic** auth.
* More advanced (but equally easy) techniques for declaring **deeply nested JSON models** (thanks to Pydantic).
* **GraphQL** integration with <a href="https://strawberry.rocks" class="external-link" target="_blank">Strawberry</a> and other libraries.

// while the recursive call using m2 will use expNNMontgomery.
// For more details, see Ç. K. Koç, “Montgomery Reduction with Even Modulus”,
// IEE Proceedings: Computers and Digital Techniques, 141(5) 314-316, September 1994.
// http://www.people.vcu.edu/~jwang3/CMSC691/j34monex.pdf
func (z nat) expNNMontgomeryEven(x, y, m nat) nat {
	// Split m = m₁ × m₂ where m₁ = 2ⁿ
	n := m.trailingZeroBits()