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+### `tf.stop_gradient(input, name=None)` {#stop_gradient}
+
+Stops gradient computation.
+
+When executed in a graph, this op outputs its input tensor as-is.
+
+When building ops to compute gradients, this op prevents the contribution of
+its inputs to be taken into account.  Normally, the gradient generator adds ops
+to a graph to compute the derivatives of a specified 'loss' by recursively
+finding out inputs that contributed to its computation.  If you insert this op
+in the graph it inputs are masked from the gradient generator.  They are not
+taken into account for computing gradients.
+
+This is useful any time you want to compute a value with TensorFlow but need
+to pretend that the value was a constant. Some examples include:
+
+*  The *EM* algorithm where the *M-step* should not involve backpropagation
+   through the output of the *E-step*.
+*  Contrastive divergence training of Boltzmann machines where, when
+   differentiating the energy function, the training must not backpropagate
+   through the graph that generated the samples from the model.
+*  Adversarial training, where no backprop should happen through the adversarial
+   example generation process.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+