it.second = strings::StrCat(
          "Operation '", op.node.DebugString(), "' was changed by ",
          mutation_type,
          " after it was run by a session. This mutation will have no effect, "
          "and will trigger an error in the future. Either don't modify "
          "nodes after running them or create a new session.");
    }
  }
}

namespace {

				}
				return
			}

			if !status.IsSuccess() {
				t.Errorf("Expected status to be success.")
			}
			if !reflect.DeepEqual(res, tt.want) {
				t.Errorf("Score map after RunScorePlugin. got: %+v, want: %+v.", res, tt.want)
			}
		})
	}
}

func TestPreFilterPlugins(t *testing.T) {
	preFilter1 := &TestPreFilterPlugin{}

			if typ.IfaceIndir() {
				// value cannot be inlined in interface data.
				// Must make a copy, because f might keep a reference to it,
				// and we cannot let f keep a reference to the stack frame
				// after this function returns, not even a read-only reference.
				v.ptr = unsafe_New(typ)
				if typ.Size() > 0 {
					typedmemmove(typ, v.ptr, add(ptr, st.stkOff, "typ.size > 0"))
				}
				v.flag |= flagIndir
			} else {

        Signature Algorithm: ED25519
        Issuer: CN = Ed25519 test certificate
        Validity
            Not Before: May  6 17:27:16 2019 GMT
            Not After : Jun  5 17:27:16 2019 GMT
        Subject: CN = Ed25519 test certificate
        Subject Public Key Info:
            Public Key Algorithm: ED25519
                ED25519 Public-Key:
                pub:

    the rank of `input`. For each dimension D of `input`, `paddings[D, 0]`
    indicates how many zeros to add before the contents of `input` in that
    dimension, and `paddings[D, 1]` indicates how many zeros to add after the
    contents of `input` in that dimension.

    The padded size of each dimension D of the output is:

      `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`

    For example:

      shape_attr = DenseIntElementsAttr::get(output_int_type, output_shape);
    }

    auto shape = rewriter.create<ConstOp>(pack_op.getLoc(), shape_attr);

    // TODO(b/173622615): Remove after fixed.
    ReplaceTfOpWithNewOp<ReshapeOp>(rewriter, pack_op, output_ty,
                                    pack_op.getOperand(0), shape);
    return success();
  }
};

// function names unchanged in an MLIR roundtrip causes test failures.
// TODO(b/142268695) Re-evaluate whether we need this class v.s. directly using
// and TF function name as MLIR function name after b/142268695 is root caused.
class NameUniquifier : public OpOrArgNameMapper {
 public:
  explicit NameUniquifier(const FunctionLibraryDefinition& flib)
      : flib_(flib) {}

 private:

			// If we don't need any other outputs, we can stop.
			// Otherwise, we need to write files to a.Objdir (needVet, needCgoHdr).
			// Remember that we might have them in cache
			// and check again after we create a.Objdir.
			cachedBuild = true
			a.output = []byte{} // start saving output in case we miss any cache results
			need &^= needBuild
			if b.NeedExport {
				p.Export = a.built
				p.BuildID = a.buildID

						Status:  "True",
						Reason:  "TerminationByKubelet",
						Message: "The node was low on resource: memory. Threshold quantity: 2Gi, available: 1500Mi. ",
					})
				}

				// verify the pod status after applying the status update function
				podKiller.statusFn(&podKiller.pod.Status)

// TFL_TransposeOp when the tensor has some dimensions with value==1
// Example- "tfl.transpose"(tensor<56x8x56x1x1x1x7xf32>, [4, 5, 1, 2, 0, 6, 3])
// Permutation before squeese is [4, 5, 1, 2, 0, 6, 3] becomes [1, 2, 0, 3]
// after squeeze is perfomed to retain the relative ordering of the non-1 dims.
DenseElementsAttr GetSqueezedPermutation(Value input_value,
                                         Value input_permutation) {