dimension if necessary. Use this mode on rank 1 inputs for the old
            `"binary"` behavior of one-hot encoding a batch of scalars.
        *   `Normalization` will no longer automatically uprank rank 1 inputs,
            allowing normalization of unbatched multi-dimensional samples.

*   `tf.lite`:

    *   The recommended Android NDK version for building TensorFlow Lite has

    TF_IntOrFpTensor:$z
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_FusedBatchNormOp : TF_Op<"FusedBatchNorm", [Pure]> {
  let summary = "Batch normalization.";

  let description = [{
Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW".
The size of 1D Tensors matches the dimension C of the 4D Tensors.
  }];

  let arguments = (ins

Class <org.gradle.normalization.internal.InputNormalizationHandlerInternal$CachedState> is not annotated (directly or via its package) with @org.gradle.api.NonNullApi in (InputNormalizationHandlerInternal.java:0)

in an auto-updating file.\n\nAlpha, gated by the ClusterTrustBundleProjection feature gate.\n\nClusterTrustBundle objects can either be selected by name, or by the combination of signer name and a label selector.\n\nKubelet performs aggressive normalization of the PEM contents written into the pod filesystem.  Esoteric PEM features such as inter-block comments and block headers are stripped.  Certificates are deduplicated. The ordering of certificates within the file is arbitrary, and Kubelet may...