name == "first"
        tracker.root
        tracker.segments.empty
        tracker.toRelativePath() == ""
    }

    def "can enter and leave second level"() {
        given:
        tracker.enter("root")
        tracker.enter("first")
        when:
        tracker.enter("second")
        then:
        !tracker.root
        tracker.segments as List == ["first", "second"]

import java.util.List;
import java.util.Set;
import java.util.stream.Collectors;

/**
 * This class keeps track of all objects being evaluated at the moment.
 * It helps to provide nicer error messages when the evaluation enters an endless cycle (A obtains a value of B which obtains a value of A).
 * <p>
 * Concurrent evaluation of same objects by multiple threads is still allowed.
 * <p>

env HOME=$WORK${/}home${/}gopher
exec git config --global --show-origin user.name
stdout 'Go Gopher'

! go mod download github.com/golang/nonexist@latest
stderr 'Confirm the import path was entered correctly.'
stderr 'If this is a private repository, see https://golang.org/doc/faq#git_https for additional information.'
! stdout .

# Fetching a nonexistent commit should return an "unknown revision"
# error message.

			switch state {
			case entered:
				if new == trace.GoWaiting {
					state = blocked
				} else {
					state = entered
				}
			case blocked:
				if new == trace.GoRunnable {
					state = runnable
				} else {
					state = entered
				}
			case runnable:
				if new == trace.GoRunning {
					state = running
				} else {
					state = entered
				}
			case running:

  } else {
    // Some control flow ops in TensorFlow Graph have their respective "Ref" ops
    // as well. For example there is Enter and RefEnter op. RefEnter forwards
    // the input ref buffer to output. However both Enter and RefEnter are
    // mapped to tf_executor::EnterOp during import. Check if it is a Ref op to
    // correctly map to the TensorFlow Graph op.
    if (IsRefTypeControlOp(inst)) op_name = "Ref";

    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape {
        }
        int_val: 0
      }
    }
  }
}
node {
  name: "Enter"
  op: "Enter"
  input: "Const"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "frame_name"
    value {
      s: "while_context"
    }
  }
  attr {

# Cluster Configuration

##### Deprecation Notice: This directory has entered maintenance mode and will not be accepting new providers. Deployments in this directory will continue to be maintained and supported at their current level of support.

    %Enter, %ctl_0 = Enter(%Const) [%arg.ctl] name("while/Enter") {T = i32, frame_name = "while/while_context", is_constant = false, parallel_iterations = 10 : i64} : (tensor<i32>) -> (tensor<*xi32>)
    %NextIteration, %ctl_1 = NextIteration(%Const) [%arg.ctl] name("while/NextIteration") {T = i32} : (tensor<i32>) -> (tensor<*xi32>)

    %Enter, %ctl_0 = Enter(%Const) name("while/Enter") {T = i32, frame_name = "while/while_context", is_constant = false, parallel_iterations = 10 : i64} : (tensor<i32>) -> (tensor<*xi32>)
    %NextIteration, %ctl_1 = NextIteration(%Add) name("while/NextIteration") {T = i32} : (tensor<*xi32>) -> (tensor<*xi32>)

        tracker.leaveDynamicCall(entryPoint2)
        tracker.leaveDynamicCall(entryPoint1)

        then:
        log == ["enter", entryPoint1, "enter", entryPoint2, "leave", entryPoint2, "leave", entryPoint1]
    }

    def "throws exception on mismatched calls"() {
        when:
        tracker.enterDynamicCall(new Object())
        tracker.leaveDynamicCall(new Object())

        then: