* **Machine Learning**: normalmente requiere muchas multiplicaciones de "matrices" y "vectores". Piensa en una enorme hoja de cálculo con números y multiplicando todos juntos al mismo tiempo.

		{math.MaxInt64, 12, true},
		{0, 12, true},
		{math.MinInt64, 12, true},
	}

	for _, v := range vectors {
		ok := fitsInBase256(v.width, v.in)
		if ok != v.ok {
			t.Errorf("fitsInBase256(%d, %d): got %v, want %v", v.in, v.width, ok, v.ok)
		}
	}
}

func TestParseNumeric(t *testing.T) {
	vectors := []struct {
		in   string
		want int64
		ok   bool
	}{
		// Test base-256 (binary) encoded values.

	"hash/crc32"
	"internal/obscuretestdata"
	"io"
	"maps"
	"math"
	"os"
	"path"
	"reflect"
	"slices"
	"strconv"
	"strings"
	"testing"
	"time"
)

func TestReader(t *testing.T) {
	vectors := []struct {
		file     string    // Test input file
		obscured bool      // Obscured with obscuretestdata package
		headers  []*Header // Expected output headers

		return 0, testError{fmt.Errorf("got Seek(%d, %d), want Seek(%d, %d)", pos, whence, s, io.SeekCurrent)}
	}
	f.pos += s
	f.ops = f.ops[1:]
	return f.pos, nil
}

func TestSparseEntries(t *testing.T) {
	vectors := []struct {
		in   []sparseEntry
		size int64

		wantValid    bool          // Result of validateSparseEntries
		wantAligned  []sparseEntry // Result of alignSparseEntries

	VFWCVTXUFV	X10, V3				// ERROR "expected vector register in vs2 position"
	VFWCVTXFV	X10, V3				// ERROR "expected vector register in vs2 position"
	VFWCVTRTZXUFV	X10, V3				// ERROR "expected vector register in vs2 position"
	VFWCVTRTZXFV	X10, V3				// ERROR "expected vector register in vs2 position"
	VFWCVTFXUV	X10, V3				// ERROR "expected vector register in vs2 position"
	VFWCVTFXV	X10, V3				// ERROR "expected vector register in vs2 position"

    BaseEncoding separated = base64().withSeparator("\n", 3);
    assertThrows(UnsupportedOperationException.class, () -> separated.withSeparator("$", 4));
  }

  public void testBase64() {
    // The following test vectors are specified in RFC 4648 itself
    testEncodingWithSeparators(base64(), "", "");
    testEncodingWithSeparators(base64(), "f", "Zg==");
    testEncodingWithSeparators(base64(), "fo", "Zm8=");

	VWADDWX		X10, V2, V4, V3			// ERROR "invalid vector mask register"
	VWSUBWV		V1, V2, V4, V3			// ERROR "invalid vector mask register"
	VWSUBWX		X10, V2, V4, V3			// ERROR "invalid vector mask register"
	VWCVTXXV	V2, V1, V3			// ERROR "invalid vector mask register"
	VWCVTUXXV	V2, V1, V3			// ERROR "invalid vector mask register"
	VZEXTVF2	V2, V3, V4			// ERROR "invalid vector mask register"

        assertFalse(Arrays.equals(signingKey, signingKey311), "Should be different from SMB 3.1.1 key");
    }

    @Test
    @DisplayName("Should derive keys with specific test vectors")
    void testDeriveKeys_TestVectors() {
        // Given - Use known test vector (simplified for demonstration)
        byte[] testSessionKey = new byte[16];
        Arrays.fill(testSessionKey, (byte) 0xAA);
        byte[] testPreauth = new byte[64];

  // Process inputs.
  std::vector<tensorflow::OutputTensor> input_tensors;
  std::unordered_map<const Node*, std::vector<int>> input_nodes;
  status->status = tensorflow::ProcessInputs(fn_body, fn_name, ninputs, inputs,
                                             &input_tensors, &input_nodes);
  if (TF_GetCode(status) != TF_OK) return nullptr;

  // Process outputs.
  std::vector<tensorflow::OutputTensor> output_tensors;

  if (!status->status.ok()) return;

  // Initialize a input_tensor vector with `nullptr` values.
  std::vector<const Tensor*> input_tensors_vector(num_inputs, nullptr);
  // A vector to keep track of newly created `tf::Tensor` objects.
  std::vector<Tensor> all_input_tensors;
  // Update the vector with information from `input_tensors` if provided.
  if (input_tensors != nullptr) {