assertEquals("9.999 \u03bcs", stopwatch.toString());
    stopwatch.reset();
    stopwatch.start();
    ticker.advance(1234567);
    assertEquals("1.235 ms", stopwatch.toString());
    stopwatch.reset();
    stopwatch.start();
    ticker.advance(5000000000L);
    assertEquals("5.000 s", stopwatch.toString());
    stopwatch.reset();
    stopwatch.start();
    ticker.advance((long) (1.5 * 60 * 1000000000L));

    A.reset(A_raw);
  }
  float B_vals[] = {.5f, -1.0f, 1.0f, 1.0f};
  int64_t B_dims[] = {2, 2};
  AbstractTensorHandlePtr B;
  {
    AbstractTensorHandle* B_raw;
    absl::Status s = TestTensorHandleWithDims<float, TF_FLOAT>(
        ctx_.get(), B_vals, B_dims, 2, &B_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    B.reset(B_raw);
  }

    assertEquals("9.999 \u03bcs", stopwatch.toString());
    stopwatch.reset();
    stopwatch.start();
    ticker.advance(1234567);
    assertEquals("1.235 ms", stopwatch.toString());
    stopwatch.reset();
    stopwatch.start();
    ticker.advance(5000000000L);
    assertEquals("5.000 s", stopwatch.toString());
    stopwatch.reset();
    stopwatch.start();
    ticker.advance((long) (1.5 * 60 * 1000000000L));

    var_to_data_type_map_.swap(result.second);
  } else {
    reader_.reset(new TensorSliceReader(filename));
    if (!reader_->status().ok()) {
      tsl::Set_TF_Status_from_Status(status, reader_->status());
      return;
    }
    var_to_shape_map_.reset(
        new TensorSliceReader::VarToShapeMap(reader_->GetVariableToShapeMap()));
    var_to_data_type_map_.reset(new TensorSliceReader::VarToDataTypeMap(

func (b *Buffer) Truncate(n int) {
	if n == 0 {
		b.Reset()
		return
	}
	b.lastRead = opInvalid
	if n < 0 || n > b.Len() {
		panic("bytes.Buffer: truncation out of range")
	}
	b.buf = b.buf[:b.off+n]
}

// Reset resets the buffer to be empty,
// but it retains the underlying storage for use by future writes.
// Reset is the same as [Buffer.Truncate](0).
func (b *Buffer) Reset() {
	b.buf = b.buf[:0]
	b.off = 0

    Range(&vals, 0, num_dims_out);
    AbstractTensorHandle* sum_dims_raw = nullptr;
    TF_RETURN_IF_ERROR(TestTensorHandleWithDims<int32_t, TF_INT32>(
        ctx, vals.data(), vals_shape, 1, &sum_dims_raw));
    sum_dims.reset(sum_dims_raw);
  }

  // Reduce sum the output on all dimensions.
  TF_RETURN_IF_ERROR(ops::Sum(ctx, model_out.get(), sum_dims.get(), &outputs[0],
                              /*keep_dims=*/false, "sum_output"));

package jstream

import (
	"unicode/utf8"
)

type scratch struct {
	data []byte
	fill int
}

// reset scratch buffer
func (s *scratch) reset() { s.fill = 0 }

// bytes returns the written contents of scratch buffer
func (s *scratch) bytes() []byte { return s.data[0:s.fill] }

// grow scratch buffer
func (s *scratch) grow() {
	ndata := make([]byte, cap(s.data)*2)
	copy(ndata, s.data)
	s.data = ndata
}

		t.Errorf("buf = %q; want foo", buf)
	}

	r.Reset(strings.NewReader("bar bar"))
	checkAll(r, "bar bar")

	*r = Reader{} // zero out the Reader
	r.Reset(strings.NewReader("bar bar"))
	checkAll(r, "bar bar")

	// Wrap a reader and then Reset to that reader.
	r.Reset(strings.NewReader("recur"))
	r2 := NewReader(r)
	checkAll(r2, "recur")
	r.Reset(strings.NewReader("recur2"))
	r2.Reset(r)

	if buf.Len() > m {
		t.Log("WARNING: TestEncodeDecodeallTierStats Msgsize() is inaccurate")
	}

	vn := allTierStats{}
	err := msgp.Decode(&buf, &vn)
	if err != nil {
		t.Error(err)
	}

	buf.Reset()
	msgp.Encode(&buf, &v)
	err = msgp.NewReader(&buf).Skip()
	if err != nil {
		t.Error(err)
	}
}

func BenchmarkEncodeallTierStats(b *testing.B) {
	v := allTierStats{}
	var buf bytes.Buffer

    ASSERT_EQ(errors::OK, s.code()) << s.message();
    ctx.reset(ctx_raw);
  }

  AbstractTensorHandlePtr t;
  {
    AbstractTensorHandle* x_raw = nullptr;
    absl::Status s =
        TestScalarTensorHandle<float, TF_FLOAT>(ctx.get(), 1.0f, &x_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    t.reset(x_raw);
  }

  AbstractOperationPtr check_numerics_op(ctx->CreateOperation());