class_docstr: Gradients Debugger.
  
  Allows retrieval of gradient tensors created by TensorFlow's automatic
  differentiation algorithm, i.e., `tf.gradients` and optimizer classes that
  use it.
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docstr: Register the gradient tensor for an x-tensor.
  
  Args:
    x_tensor_name: (`str`) the name of the independent `tf.Tensor`, i.e.,
      the tensor on the denominator of the differentiation.
    gradient_tensor: the gradient `tf.Tensor`.
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code: def register_gradient_tensor(self, x_tensor_name, gradient_tensor):
      """Register the gradient tensor for an x-tensor.
  
      Args:
        x_tensor_name: (`str`) the name of the independent `tf.Tensor`, i.e.,
          the tensor on the denominator of the differentiation.
        gradient_tensor: the gradient `tf.Tensor`.
      """
      if len(_gradient_debuggers) == 1 or self._is_active_context:
          self._check_same_graph(gradient_tensor)
          self._gradient_tensors[x_tensor_name] = gradient_tensor
  
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 10 for calling the GradientsDebugger obj's underlying member method code with example usage: obj.register_gradient_tensor(x_tensor_name, gradient_tensor) without return types