edium}.uk-animation-slide-left-medium{animation-name:uk-fade-left-medium}.uk-animation-slide-right-medium{animation-name:uk-fade-right-medium}.uk-animation-kenburns{animation-name:uk-scale-kenburns;animation-duration:15s}.uk-animation-shake{animation-name:uk-shake}.uk-animation-stroke{animation-name:uk-stroke;stroke-dasharray:var(--uk-animation-stroke);animation-duration:2s}.uk-animation-reverse{animation-direction:reverse;animation-timing-function:ease-in}.uk-animation-fast{animation-duration:....

}
func.func @WhitespaceTokenize_RaggedConcat_assert_equal_1_Assert_AssertGuard_false_3210(%arg0: tensor<i1>, %arg1: tensor<i64>, %arg2: tensor<i64>) -> tensor<i1> attributes {tf._input_shapes = [#tf_type.shape<>, #tf_type.shape<>, #tf_type.shape<>], tf.signature.is_stateful} {
  %0 = "tf.Const"() {value = dense<"Input tensors have incompatible shapes."> : tensor<!tf_type.string>} : () -> tensor<!tf_type.string>

    if (ShapedType::isDynamic(num_index_dims)) return failure();

    auto updates = op.getUpdates();

    // Broadcast scalar `updates` in into expected shape as following shape:
    // updates.shape == indices.shape[:-1] + tensor.shape[indices.shape[-1]:]
    if (updates_ty.getRank() == 0 &&
        (std::is_same<OpTy, TF::TensorScatterUpdateOp>::value ||
         std::is_same<OpTy, TF::TensorScatterAddOp>::value)) {

  // CHECK-NEXT: %[[SHAPE0:.*]] = shape.shape_of %arg0 : tensor<?xi1> -> tensor<1xindex>
  // CHECK-NEXT: %[[SHAPE1:.*]] = shape.shape_of %arg1 : tensor<?x?x8xi32> -> tensor<3xindex>
  // CHECK-NEXT: %[[SHAPE2:.*]] = shape.shape_of %arg2 : tensor<?x?x8xi32> -> tensor<3xindex>
  // CHECK-NEXT: %[[SHAPEEQ1:.*]] = shape.cstr_eq %[[SHAPE1]], %[[SHAPE2]] : tensor<3xindex>, tensor<3xindex>

func.func @ReorderReshapex2Add(%arg0: tensor<1x2x3x4xf32>, %arg1: tensor<1x2x3x4xf32>) -> tensor<6x4xf32> {
  %shape = arith.constant dense<[6, 4]> : tensor<2xi32>
  %0 = "tfl.reshape"(%arg0, %shape) : (tensor<1x2x3x4xf32>, tensor<2xi32>) -> tensor<6x4xf32>
  %1 = "tfl.reshape"(%arg1, %shape) : (tensor<1x2x3x4xf32>, tensor<2xi32>) -> tensor<6x4xf32>

// CHECK:           %7 = "tf.Shape"(%arg0) : (tensor<4x4x256xf32>) -> tensor<3xi32>
// CHECK:           %8 = "tf.Shape"(%arg1) : (tensor<4x?x256xf32>) -> tensor<3xi32>
// CHECK-DAG:       %cst_5 = "tf.Const"() <{value = dense<[0, 1]> : tensor<2xi64>}> : () -> tensor<2xi64>

- Multiple Kubernetes clusters can now share the same resource group
    - When upgrading from previous releases, issues will arise with public IPs if multiple clusters share the same resource group. To solve these problems, make the following changes to the cluster:

- Failed to provision volume with StorageClass "azurefile-premium": failed to create share andy-mg1121-dynamic-pvc-1a7b2813-d1b7-11e8-9e96-000d3a03e16b in account f7228f99bcde411e8ba4900: failed to create file share, err: storage: service returned error: StatusCode=400, ErrorCode=InvalidHeaderValue, ErrorMessage=The value for one of the HTTP headers is not in the correct format. ([#69718](https://...

message AzureFilePersistentVolumeSource {
  // secretName is the name of secret that contains Azure Storage Account Name and Key
  optional string secretName = 1;

  // shareName is the azure Share Name
  optional string shareName = 2;

  // readOnly defaults to false (read/write). ReadOnly here will force
  // the ReadOnly setting in VolumeMounts.
  // +optional
  optional bool readOnly = 3;

// NFS volumes do not support ownership management or SELinux relabeling.
type NFSVolumeSource struct {
	// Server is the hostname or IP address of the NFS server
	Server string

	// Path is the exported NFS share
	Path string

	// Optional: Defaults to false (read/write). ReadOnly here will force
	// the NFS export to be mounted with read-only permissions
	// +optional
	ReadOnly bool
}