code: def gradient_values_from_dump(grad_debugger, x_tensor, dump):
      """Find gradient values from a `DebugDumpDir` object.
  
      Args:
        grad_debugger: the `tf_debug.GradientsDebugger` instance to be used.
        x_tensor: (`tf.Tensor`, `tf.Variable` or `str`) The x-tensor object or its
          name. x-tensor refers to the independent `tf.Tensor`, i.e., the tensor
          on the denominator of the differentiation.
        dump: A `tfdbg.DebugDumpDir` object.
  
      Returns:
        If this `GradientsDebugger` instance has the gradient tensor of `x_tensor`
          registered: a list of `numpy.ndarray` representing the value of the
          gradient tensor from `dump`. The list could be empty, if the gradient
          tensor is not executed in the `tf.Session.run()` call that generated
          the `dump`. The list could also contain multiple values of the gradient
          tensor, e.g., if gradient tensor is computed repeatedly in a
          `tf.while_loop` during the run that generated the `dump`.
  
      Raises:
        LookupError: If this `GradientsDebugger` instance does not have the
          gradient tensor of `x_tensor` registered.
        ValueError: If this `GradientsDebugger` has a `tf.Graph` object that
          does not match the `tf.Graph` object of the `dump`.
        TypeError: If `x_tensor` is not a `tf.Tensor`, `tf.Variable` or `str`.
      """
      # TODO(cais): Use this method in LocalCLIDebugWrapperSession to present the
      # gradient tensors to the TFDBG CLI.
  
      # If possible, verify that the Python graph of the dump and that of this
      # GradientsDebugger match.
      if (
          dump.python_graph
          and grad_debugger.graph
          and dump.python_graph != grad_debugger.graph
      ):
          raise ValueError(
              "This GradientsDebugger instance has a graph (%s) that differs from "
              "the graph of the DebugDumpDir object (%s)."
              % (grad_debugger.graph, dump.python_graph)
          )
  
      gradient_tensor = grad_debugger.gradient_tensor(x_tensor)
      node_name, output_slot = debug_graphs.parse_node_or_tensor_name(
          gradient_tensor.name
      )
  
      try:
          return dump.get_tensors(
              node_name, output_slot, "DebugIdentity"
          )
      except debug_data.WatchKeyDoesNotExistInDebugDumpDirError:
          return []
  
imports: import re
  import uuid
  from tensorflow.python.debug.lib import debug_data
  from tensorflow.python.debug.lib import debug_graphs
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor
  from tensorflow.python.ops import gen_array_ops
  from tensorflow.python.ops import variables
label_desc: Use image node_id 6 for calling a global function with example usage: gradient_values_from_dump(grad_debugger, x_tensor, dump) and returns: dump, list