# Mapping of signature def key -> SignatureDef.
_SignatureDefMap = Mapping[str, meta_graph_pb2.SignatureDef]

# Default minimum number of elements in the weights for them to be quantized
# during dynamic range quantization (DRQ) and weight-only quantization.
_DYNAMIC_RANGE_DEFAULT_MIN_NUM_ELEMENTS_FOR_WEIGHTS = 1024


def _is_qat_saved_model(saved_model_path: str):

						{Name: names.PodTopologySpread, Weight: ptr.To[int32](2)},
						{Name: names.InterPodAffinity, Weight: ptr.To[int32](2)},
						{Name: names.DefaultPreemption},
						{Name: names.NodeResourcesBalancedAllocation, Weight: ptr.To[int32](1)},
						{Name: names.ImageLocality, Weight: ptr.To[int32](1)},
						{Name: names.DefaultBinder},
					},
				},
			},
		},
		{
			name: "Feature gate DynamicResourceAllocation enabled",

					{
						Name:   "cpu",
						Weight: 1,
					},
					{
						Name:   "memory",
						Weight: 1,
					},
				},
			},
		},
		"invalid config": {
			args: &config.NodeResourcesBalancedAllocationArgs{
				Resources: []config.ResourceSpec{
					{
						Name:   "cpu",
						Weight: 2,
					},
					{
						Name:   "memory",
						Weight: 1,
					},
				},
			},

  BroadcastBatchDimensionsForBatchMatMul(builder, loc, input, weight);

  // Both input and weight have the same rank after broadcasting.
  ShapedType weight_shape = mlir::cast<ShapedType>(weight.getType());
  int num_batch_dim = weight_shape.getRank() - 2;

  // Transpose and constant-fold the weight if needed.
  if (adj_y.getValue()) {
    SmallVector<int32_t> perm_values(num_batch_dim);
    absl::c_iota(perm_values, 0);

									{Name: names.VolumeZone},
									{Name: names.PodTopologySpread, Weight: ptr.To[int32](2)},
									{Name: names.InterPodAffinity, Weight: ptr.To[int32](2)},
									{Name: names.DefaultPreemption},
									{Name: names.NodeResourcesBalancedAllocation, Weight: ptr.To[int32](1)},
									{Name: names.ImageLocality, Weight: ptr.To[int32](1)},
									{Name: names.DefaultBinder},
								},
							},
							Bind: configv1.PluginSet{

  OpSet op_set_;

  Option<bool> enable_per_channel_quantization_{
      *this, "enable-per-channel-quantization", llvm::cl::init(false),
      llvm::cl::desc("Whether enable per-channel quantized weights.")};
};

// If the weight is applicable to dynamic range quantization, insert Quantize
// and Dequantize ops with per-tensor scale.
class PrepareDRQQuantizableOp : public OpRewritePattern<arith::ConstantOp> {
 public:

VirtualService: bookinfo
   Route to host "productpage" with weight 30%
   Route to host "productpage2" with weight 20%
   Route to host "productpage3" with weight 50%
   Match: /prefix*
--------------------
Exposed on Ingress Gateway http://1.1.1.1
Exposed on Ingress Gateway http://2.2.2.2
VirtualService: bookinfo
   Route to host "productpage" with weight 30%
   Route to host "productpage2" with weight 20%

}

ul {
    list-style-type: disc;
}

ol {
    list-style-type: decimal;
}

dl dt {
    font-weight: normal;
}

tfoot {
    font-style: italic;
}

caption {
    background: #eee;
}

abbr, acronym {
    font-size: 85%;
    font-weight: bold;
    color: #555;
    text-transform: uppercase;
}

abbr[title], acronym[title] {
    cursor: help;

// 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>

		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()