return op->emitOpError()
             << "output #" << i << " should be " << expected_output_type
             << " instead got " << output.getType();
  }
  return success();
}

mlir::LogicalResult SplitOp::verify() {
  SplitOp op = *this;
  int64_t num_splits = op.getNumSplits();
  if (op.getNumResults() != num_splits)

          inputs={'x1': in_placeholder_1}, outputs={'output1': output_tensor_1}
      )

      in_placeholder_2, output_tensor_2 = self._create_simple_tf1_conv_model()
      sig_def_2 = signature_def_utils_impl.predict_signature_def(
          inputs={'x2': in_placeholder_2}, outputs={'output2': output_tensor_2}
      )

        throw new MyRuntimeException();
      }
    }
    Fallback fallback = new Fallback();
    SettableFuture<Object> input = SettableFuture.create();

    ListenableFuture<Object> output = catching(input, Throwable.class, fallback, directExecutor());
    fallback.output = output;

    input.setException(new MyException());
    assertTrue(output.isCancelled());
  }

        throw new MyRuntimeException();
      }
    }
    Fallback fallback = new Fallback();
    SettableFuture<Object> input = SettableFuture.create();

    ListenableFuture<Object> output = catching(input, Throwable.class, fallback, directExecutor());
    fallback.output = output;

    input.setException(new MyException());
    assertTrue(output.isCancelled());
  }

		add chain ip kube-proxy filter-output { type filter hook output priority -110 ; }
		add rule ip kube-proxy filter-output ct state new jump service-endpoints-check
		add rule ip kube-proxy filter-output ct state new jump firewall-check
		add chain ip kube-proxy filter-output-post-dnat { type filter hook output priority -90 ; }
		add rule ip kube-proxy filter-output-post-dnat ct state new jump cluster-ips-check

  return rewriter.create<arith::ConstantOp>(output.getLoc(), attr_type, attr);
}

Value ExpandedDynamicShape(PatternRewriter& rewriter, Value input,
                           DenseIntElementsAttr broadcast_dimensions,
                           Value output) {
  assert(mlir::cast<ShapedType>(output.getType()) &&
         "output type must be of ShapedType");

    }

    func.return %1 : tensor<2xi32>
  }

  //  Tests that all inputs to a function are passed and all outputs are returned for an outside compilation cluster
  //  with dynamic-shaped inputs/outputs. This test case tests that if another outside compiled returns
  //  only statically-shaped outputs, the value consumed by an outside compiled op with some dynamically shaped inputs is
  //  sent back to the host.

}

void CreateSubgraphTouchingRefVar(const Scope& s) {
  Output variable =
      ops::Variable(s.WithOpName("variable"), PartialTensorShape{}, DT_FLOAT);
  Output read = ops::Identity(s.WithOpName("read_ref_var"), variable);
  Output neg = ops::Negate(s.WithOpName("negate_ref"), read);
  Output add = ops::Add(s.WithOpName("add_ref"), neg, neg);

  Output constant =

    return op.emitError() << "output 'sparse_indices' should have same length "
                          << "as attribute 'sparse_types'";
  }
  if (op.getSparseShapes().size() != sparse_types_count) {
    return op.emitError() << "output 'sparse_shapes' should have same length "
                          << "as attribute 'sparse_types'";
  }

  // Validate ragged variadic output lengths.

  // expected-error @+1 {{rank of input tensor is 2. rank of output tensor is expected to be 1, instead of 2.}}
  %0 = "tf.Bitcast"(%arg0) : (tensor<3x2xi16>) -> tensor<3x2xi32>
  func.return %0 : tensor<3x2xi32>
}

// -----

func.func @testBitcast_v5(%arg0: tensor<3x2xi16>) -> tensor<2xi32> {
  // expected-error @+1 {{the 0th dim of output tensor is 2. It is not equal to the one in input tensor, which is 3}}