as C.stdout, or functions such as C.putchar.

If the import of "C" is immediately preceded by a comment, that
comment, called the preamble, is used as a header when compiling
the C parts of the package. For example:

	// #include <stdio.h>
	// #include <errno.h>
	import "C"

The preamble may contain any C code, including function and variable

	// However, if the strings require multibyte UTF-8 encoding and is a
	// valid UTF-8 string, then we set the UTF-8 bit.
	//
	// For the case, where the user explicitly wants to specify the encoding
	// as UTF-8, they will need to set the flag bit themselves.
	utf8Valid1, utf8Require1 := detectUTF8(fh.Name)
	utf8Valid2, utf8Require2 := detectUTF8(fh.Comment)
	switch {
	case fh.NonUTF8:

	b.WriteString("z")
	if err := b.Flush(); err != nil {
		t.Error("WriteString", err)
	}
	s := "01234567890abcdefghijklmnopqrstuvwxyz"
	if buf.String() != s {
		t.Errorf("WriteString wants %q gets %q", s, buf.String())
	}
}

func TestWriteStringStringWriter(t *testing.T) {
	const BufSize = 8
	{
		tw := &teststringwriter{}
		b := NewWriterSize(tw, BufSize)
		b.WriteString("1234")

float64     the set of all IEEE 754 64-bit floating-point numbers

complex64   the set of all complex numbers with float32 real and imaginary parts
complex128  the set of all complex numbers with float64 real and imaginary parts

byte        alias for uint8
rune        alias for int32
</pre>

<p>
The value of an <i>n</i>-bit integer is <i>n</i> bits wide and represented using

t-asm/cryptogams/blob/master/ppc/aesp8-ppc.pl // Some function names were changed to be consistent with Go function // names. For instance, function aes_p8_set_{en,de}crypt_key become // set{En,De}cryptKeyAsm. I also split setEncryptKeyAsm in two parts // and a new session was created (doEncryptKeyAsm). This was necessary to // avoid arguments overwriting when setDecryptKeyAsm calls setEncryptKeyAsm. // There were other modifications as well but kept the same functionality. #include "textflag.h"...

        requires all optimizer variables to be created at the first
        `apply_gradients()` or `minimize()` call. If your workflow calls
        optimizer to update different parts of model in multiple stages,
        please call `optimizer.build(model.trainable_variables)` before the
        training loop.
    *   **Performance regression on `ParameterServerStrategy`.** This could be