n, b.err = b.wr.Write(p)
		} else {
			n = copy(b.buf[b.n:], p)
			b.n += n
			b.Flush()
		}
		nn += n
		p = p[n:]
	}
	if b.err != nil {
		return nn, b.err
	}
	n := copy(b.buf[b.n:], p)
	b.n += n
	nn += n
	return nn, nil
}

// WriteByte writes a single byte.
func (b *Writer) WriteByte(c byte) error {
	if b.err != nil {
		return b.err
	}

// io.EOF when it is hit before len(b) bytes are read.
func tryReadFull(r io.Reader, b []byte) (n int, err error) {
	for len(b) > n && err == nil {
		var nn int
		nn, err = r.Read(b[n:])
		n += nn
	}
	if len(b) == n && err == io.EOF {
		err = nil
	}
	return n, err
}

// readSpecialFile is like io.ReadAll except it returns

*   [C/C++ license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op.cc#L1)
*   [Python license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/nn.py#L1)
*   [Java license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/java/src/main/java/org/tensorflow/Graph.java#L1)

#include "tensorflow/c/eager/gradients.h"
#include "tensorflow/c/experimental/ops/array_ops.h"
#include "tensorflow/c/experimental/ops/math_ops.h"
#include "tensorflow/c/experimental/ops/nn_ops.h"

using std::vector;
using tensorflow::ops::AddV2;
using tensorflow::ops::Div;
using tensorflow::ops::DivNoNan;
using tensorflow::ops::MatMul;
using tensorflow::ops::Mul;
using tensorflow::ops::Neg;

        "//tensorflow/core:framework_internal",
        "//tensorflow/core:functional_ops_op_lib",
        "//tensorflow/core:lib",
        "//tensorflow/core:math_ops_op_lib",
        "//tensorflow/core:nn_ops_op_lib",
        "//tensorflow/core:no_op_op_lib",
        "//tensorflow/core:protos_all_cc",
        "//tensorflow/core:sendrecv_ops_op_lib",
        "//tensorflow/core:spectral_ops_op_lib",