}
		paletted = png.(*image.Paletted)
	}

	// Write the filename and IHDR.
	io.WriteString(w, "#SNG: from "+filename+".png\nIHDR {\n")
	fmt.Fprintf(w, "    width: %d; height: %d; bitdepth: %d;\n", bounds.Dx(), bounds.Dy(), bitdepth)
	if s, ok := fakeIHDRUsings[filename]; ok {
		io.WriteString(w, s)
	} else {
		switch {
		case cm == color.RGBAModel, cm == color.RGBA64Model:
			io.WriteString(w, "    using color;\n")

example, suppose y = f(x) and we wish to apply a custom function g for backprop
such that dx = g(dy). In Python,

```python
with tf.get_default_graph().gradient_override_map(
    {'IdentityN': 'OverrideGradientWithG'}):
  y, _ = identity_n([f(x), x])

@tf.RegisterGradient('OverrideGradientWithG')
def ApplyG(op, dy, _):
  return [None, g(dy)]  # Do not backprop to f(x).
```
  }];

  let arguments = (ins

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// compatibility. The NewXxx functions do not return an error.
func pixelBufferLength(bytesPerPixel int, r Rectangle, imageTypeName string) int {
	totalLength := mul3NonNeg(bytesPerPixel, r.Dx(), r.Dy())
	if totalLength < 0 {
		panic("image: New" + imageTypeName + " Rectangle has huge or negative dimensions")
	}
	return totalLength
}

// RGBA is an in-memory image whose At method returns [color.RGBA] values.

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github.com/prometheus/client_model v0.6.0 h1:k1v3CzpSRUTrKMppY35TLwPvxHqBu0bYgxZzqGIgaos=
github.com/prometheus/client_model v0.6.0/go.mod h1:NTQHnmxFpouOD0DpvP4XujX3CdOAGQPoaGhyTchlyt8=
github.com/prometheus/common v0.48.0 h1:QO8U2CdOzSn1BBsmXJXduaaW+dY/5QLjfB8svtSzKKE=
github.com/prometheus/common v0.48.0/go.mod h1:0/KsvlIEfPQCQ5I2iNSAWKPZziNCvRs5EC6ILDTlAPc=
github.com/prometheus/procfs v0.12.0 h1:jluTpSng7V9hY0O2R9DzzJHYb2xULk9VTR1V1R/k6Bo=

github.com/prometheus/client_model v0.6.0 h1:k1v3CzpSRUTrKMppY35TLwPvxHqBu0bYgxZzqGIgaos=
github.com/prometheus/client_model v0.6.0/go.mod h1:NTQHnmxFpouOD0DpvP4XujX3CdOAGQPoaGhyTchlyt8=
github.com/prometheus/common v0.48.0 h1:QO8U2CdOzSn1BBsmXJXduaaW+dY/5QLjfB8svtSzKKE=
github.com/prometheus/common v0.48.0/go.mod h1:0/KsvlIEfPQCQ5I2iNSAWKPZziNCvRs5EC6ILDTlAPc=
github.com/prometheus/procfs v0.12.0 h1:jluTpSng7V9hY0O2R9DzzJHYb2xULk9VTR1V1R/k6Bo=

                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value y = op.getY();
    Value dy = op.getDy();
    auto tp_y = mlir::dyn_cast<RankedTensorType>(y.getType());
    auto tp_dy = mlir::dyn_cast<RankedTensorType>(dy.getType());
    if (!tp_y || !tp_dy) return failure();

    // TODO(disc): Remove this constraint once fold and canonicalization
    // implemented.

u.area/l):0);u.z=!0,u.dx+=e.x+e.dx-a,e.y+=l,e.dy-=l}else{for((r||l>e.dx)&&(l=e.dx);++i<o;)u=n[i],u.x=a,u.y=s,u.dx=l,s+=u.dy=Math.min(e.y+e.dy-s,l?c(u.area/l):0);u.z=!1,u.dy+=e.y+e.dy-s,e.x+=l,e.dx-=l}}function i(r){var u=o||a(r),i=u[0];return i.x=0,i.y=0,i.dx=s[0],i.dy=s[1],o&&a.revalue(i),n([i],i.dx*i.dy/i.value),(o?e:t)(i),h&&(o=u),u}var o,a=Xo.layout.hierarchy(),c=Math.round,s=[1,1],l=null,f=Ti,h=!1,g="squarify",p=.5*(1+Math.sqrt(5));return i.size=function(n){return arguments.length?(s=n,i):s...

    // The expected graph looks like this if grad_inputs_provided.
    // If grad_inputs_provided is false, Const_0 will be a OnesLike op.
    //      ^             ^
    //    dy|           dx|        // MatMul Gradient Graph
    //      |             |
    //   MatMul_2      MatMul_1
    //   ^   ^          ^    ^
    //   |   |----------|    |
    //   |        ^          |