":{"mix-blend-mode":"plus-lighter"}})},boxShadow:(()=>{let i=Ge("boxShadow"),e=["var(--tw-ring-offset-shadow, 0 0 #0000)","var(--tw-ring-shadow, 0 0 #0000)","var(--tw-shadow)"].join(", ");return function({matchUtilities:t,addDefaults:r,theme:n}){r("box-shadow",{"--tw-ring-offset-shadow":"0 0 #0000","--tw-ring-shadow":"0 0 #0000","--tw-shadow":"0 0 #0000","--tw-shadow-colored":"0 0 #0000"}),t({shadow:a=>{a=i(a);let s=yi(a);for(let o of s)!o.valid||(o.color="var(--tw-shadow-color)");return{"@defaults...

      %6 = mhlo.add %arg1, %arg2 : tensor<f32>
      "mhlo.return"(%6) : (tensor<f32>) -> ()
    }) {
    base_dilations = dense<1> : tensor<4xi64>,
    padding = dense<[[0, 0], [0, 0], [0, 0], [0, 0]]> : tensor<4x2xi64>,
    window_dilations = dense<1> : tensor<4xi64>,
    window_dimensions = dense<[1, 3, 3, 1]> : tensor<4xi64>,

  static Value GetInitialValue(Type reduce_element_type, Location loc,
                               PatternRewriter *rewriter) {
    // The neutral element of fp addition is -0.0, not 0.0: '0.0 + -0.0 = 0.0'.
    return GetScalarNegZeroOfType(reduce_element_type, loc, rewriter);
  }
};

// Converts Max op to HLO Reduce op.
//
//   %init = arith.constant dense<...> : tensor<T>

  // CHECK: mhlo.convolution(%arg0, %arg1) dim_numbers = [b, 0, 1, 2, f]x[0, 1, 2, i, o]->[b, 0, 1, 2, f], window = {stride = [3, 1, 1], pad = {{\[\[}}0, 0], {{\[}}0, 0], {{\[}}0, 0]], lhs_dilate = [4, 1, 1], rhs_dilate = [1, 1, 1]} {batch_group_count = 2 : i64, feature_group_count = 1 : i64, precision_config = []} : (tensor<8x4x16x16x16xf32>, tensor<4x3x3x16x16xf32>) -> tensor<4x4x14x14x16xf32>

</pre>

<p>
For floating-point arguments negative zero, NaN, and infinity the following rules apply:
</p>

<pre>
   x        y    min(x, y)    max(x, y)

  -0.0    0.0         -0.0          0.0    // negative zero is smaller than (non-negative) zero
  -Inf      y         -Inf            y    // negative infinity is smaller than any other number