}
  return attr;
}

TensorId ToTensorId(const TF_Output& output) {
  return TensorId(output.oper->node.name(), output.index);
}

#if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
std::vector<tensorflow::Output> OutputsFromTFOutputs(TF_Output* tf_outputs,
                                                     int n) {
  std::vector<tensorflow::Output> outputs(n);
  for (int i = 0; i < n; ++i) {
    outputs[i] =

                    def input = inputArtifact.get().asFile
                    def output = outputs.file(input.name + ".txt")
                    assert output.parentFile.directory && output.parentFile.list().length == 0
                    println "Transforming \${input.name} to \${output.name}"
                    output.text = String.valueOf(input.length())
                }
            }
        """
    }

                        ${
            // Do not check for an empty output for incremental transforms
            incremental
                ? ""
                : "assert output.directory && output.list().length == 0"
        }
                        new File(output, "child.txt").text = "transformed"
                    }
                    def outputDirectory = output.parentFile

  let summary = [{While loop}];

  let description = [{
    output = input; while (cond(output)) { output = body(output) }

    While loop where all values are passes through arguments with implicit
    capture.

    input: A list of input tensors whose types are T.
    output: A list of output tensors whose types are T.
    cond: A region that takes 'input' and returns a boolean scalar tensor.

        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

        Returns:
          A map of: output key -> output result.
        """
        if math_ops.reduce_sum(x) > 10.0:
          out = math_ops.matmul(x, self.filters_0)
          out = nn_ops.bias_add(out, self.bias_0)
          return {'output': out}

        out = math_ops.matmul(x, self.filters_1)
        out = nn_ops.bias_add(out, self.bias_1)
        return {'output': out}

    model = IfModel()

  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");

  if (output_ty.getRank() != expected_output_rank)
    return op.emitOpError()
           << "found invalid output rank, expected " << expected_output_rank
           << " but got " << output_ty.getRank();

  // Check output batch dim with potential broadcasting.
  ArrayRef<int64_t> output_shape = output_ty.getShape();
  for (int i = 0; i < result_batch_shape.size(); ++i) {