// RUN:   -split-input-file -verify-diagnostics | FileCheck %s

// Tests that a `convolution(%activation, %weight)` with the activation tensor
// NCHW format is converted to NHWC convolution. Transpose ops are inserted to
// the activation and output to match the function signature. The weight
// constant is transposed.

// CHECK-LABEL: nchw_conv
// CHECK-SAME: %[[ARG:.+]]: tensor<1x8x4x4xf32>

  %7 = "quantfork.dcast"(%6) : (tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>) -> tensor<*xf32>
  %weight = arith.constant dense<1.0> : tensor<144x12xf32>
  %q_weight = "quantfork.qcast"(%weight) : (tensor<144x12xf32>) -> tensor<144x12x!quant.uniform<i8:f32, 0.074855112561992565:-1>>

		PrioritizeVerb: "prioritize",
		BindVerb:       "bar",
		Weight:         1,
	})

	validPlugins := validConfig.DeepCopy()
	validPlugins.Profiles[0].Plugins.Score.Enabled = append(validPlugins.Profiles[0].Plugins.Score.Enabled, config.Plugin{Name: "PodTopologySpread", Weight: 2})

	invalidPlugins := validConfig.DeepCopy()

        %scale = "tf.Const"() {value = dense<2.0> : tensor<f32>} : () -> tensor<f32>
        %weight = "tf.Const"() {value = dense<1> : tensor<1024x24x24x3xi8>} : () -> tensor<1024x24x24x3xi8>
        %input_i32 = "tf.Cast"(%weight) : (tensor<1024x24x24x3xi8>) -> tensor<1024x24x24x3xi32>
        %output = "tf.Sub"(%input_i32, %zp) : (tensor<1024x24x24x3xi32>, tensor<i32>) -> tensor<1024x24x24x3xi32>

	border-width: 1px 0 0;
	clear: both;
	margin: 1.25em 0 1.1875em;
	height: 0;
}

/* Helpful Typography Defaults */
em,
i {
	font-style: italic;
	line-height: inherit;
}

strong,
b {
	font-weight: bold;
	line-height: inherit;
}

small {
	font-size: 60%;
	line-height: inherit;
}

code {
	font-family: "Droid Sans Mono", "DejaVu Sans Mono", monospace;

					Prioritizers: []tf.PriorityConfig{{Function: tf.Node1PrioritizerExtender, Weight: 10}},
					Weight:       1,
				},
				{
					ExtenderName: "FakeExtender2",
					Predicates:   []tf.FitPredicate{tf.TruePredicateExtender},
					Prioritizers: []tf.PriorityConfig{{Function: tf.Node2PrioritizerExtender, Weight: 10}},
					Weight:       5,
				},
			},
			nodes: []string{"node1", "node2"},

func.func private @conv(%input: tensor<1x3x4x3xf32> {tf._user_specified_name = "input_tensor"}) -> tensor<*xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<1x3x4x3>]} {
  %weight = arith.constant dense_resource<__elided__> : tensor<2x3x3x2xf32>
  %bias = arith.constant dense<[7.11401462, 7.05456924]> : tensor<2xf32>

          },
          "weight": {
            "default": 0,
            "description": "Weight associated with matching the corresponding nodeSelectorTerm, in the range 1-100.",
            "format": "int32",
            "type": "integer"
          }
        },
        "required": [
          "weight",
          "preference"
        ],

// RUN: stablehlo-quant-opt %s -split-input-file -verify-diagnostics \
// RUN:     -stablehlo-quantize-composite-functions | FileCheck --check-prefix=CHECK %s

// Test that per-tensor weight-only quantized dot_general op is produced when
// empty `weight_only_ptq` is provided.

module attributes {tf_saved_model.semantics} {
  func.func private @quantize_dot_general_per_tensor(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> attributes {tf._original_func_name = "main_0"} {

				Weight:      55,
			}, {
				Destination: &networking.Destination{Host: "foo.baz.east"},
				Weight:      50,
			}},
		}, valid: true},
		{name: "total weight < 100", route: &networking.HTTPRoute{
			Route: []*networking.HTTPRouteDestination{{
				Destination: &networking.Destination{Host: "foo.baz.south"},
				Weight:      49,
			}, {