* **Machine Learning**: normalmente requiere muchas multiplicaciones de "matrices" y "vectores". Imagina en una enorme hoja de cálculo con números y tener que multiplicarlos todos 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.

    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=");

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

func TestReader(t *testing.T) {
	vectors := []struct {
		file    string    // Test input file
		headers []*Header // Expected output headers
		chksums []string  // CRC32 checksum of files, leave as nil if not checked
		err     error     // Expected error to occur

  }
}

absl::optional<std::vector<std::unique_ptr<ParallelTensor>>>
ParallelDevice::Join(
    const std::vector<PartialTensorShape>& expected_output_shapes,
    TF_Status* status) const {
  absl::optional<std::vector<std::unique_ptr<ParallelTensor>>> result;
  // Compute per-device per-output tensors
  std::vector<std::vector<TensorHandlePtr>> per_device_output_tensors;

		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

  absl::Status Shape(const std::vector<int64_t>** shape) const;
  TF_DataType dtype() const { return dtype_; }

  // Sets its output argument to a summary of the values of this tensor on every
  // component device.
  absl::Status SummarizeValue(std::string& summary);

  std::vector<TensorHandlePtr> release_tensors() { return std::move(tensors_); }

  std::vector<TFE_TensorHandle*> tensors() const {

      parallel_device.Execute(context.get(), std::vector<ParallelTensor*>(),
                              "VarHandleOp", TFE_OpGetAttrs(handle_op.get()),
                              /*expected_max_outputs=*/1, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  const std::vector<std::unique_ptr<ParallelTensor>>& handles = *outputs;
  std::vector<ParallelTensor*> handle_inputs;

    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=");

  // 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;