bool RecursiveCompilabilityChecker::OpIsInaccurate(const Node& node) const {
  // b/127344411: SelfAdjointEigV2 and Svd precision issues.
  return node.type_string() == "SelfAdjointEigV2" ||
         node.type_string() == "Svd";
}

bool RecursiveCompilabilityChecker::OpIsSlow(const Node& node) const {
  // b/128001705: SelfAdjointEigV2 and Svd performance issues.
  // b/135640736: MatrixInverse performance issues.

				"application/vnd.sun.xml.writer",
				"application/vnd.sun.xml.writer.global",
				"application/vnd.sun.xml.writer.template",
				"application/vnd.sun.wadl+xml",
				"application/vnd.sus-calendar",
				"application/vnd.svd",
				"application/vnd.swiftview-ics",
				"application/vnd.symbian.install",
				"application/vnd.syncml+xml",
				"application/vnd.syncml.dm+wbxml",
				"application/vnd.syncml.dm+xml",

      "StatelessRandomUniformFullInt",
      "StatelessRandomUniformFullIntV2",
      "StatelessTruncatedNormal",
      "StatelessTruncatedNormalV2",
      "StatelessWhile",
      "StochasticCastToInt",
      "Svd",
      "SymbolicGradient",
      "TensorArrayCloseV3",
      "TensorArrayConcatV3",
      "TensorArrayGatherV3",
      "TensorArrayGradV3",
      "TensorArrayReadV3",
      "TensorArrayScatterV3",

  <mime-type type="application/vnd.sus-calendar">
    <glob pattern="*.sus"/>
    <glob pattern="*.susp"/>
  </mime-type>
  <mime-type type="application/vnd.svd">
    <glob pattern="*.svd"/>
  </mime-type>
  <mime-type type="application/vnd.swiftview-ics"/>

  <mime-type type="application/vnd.symbian.install">
    <magic priority="50">

*   Introducing `core/util/tensor_bundle` module: a module to efficiently
    serialize/deserialize tensors to disk. Will be used in TF's new checkpoint
    format.
*   Added tf.svd for computing the singular value decomposition (SVD) of dense
    matrices or batches of matrices (CPU only).
*   Added gradients for eigenvalues and eigenvectors computed using
    `self_adjoint_eig` or `self_adjoint_eigvals`.

      "BoolAttr":$keep_dims)>
  ];

  let hasFolder = 1;
}

def TF_SvdOp : TF_Op<"Svd", [Pure]> {
  let summary = [{
Computes the singular value decompositions of one or more matrices.
  }];

  let description = [{
Computes the SVD of each inner matrix in `input` such that
`input[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :, :])`