Fix typo: compatbility => compatibility

tensorflow/python/keras/callbacks.py

@@ -833,7 +833,7 @@ class TensorBoard(Callback):
   Raises:
       ValueError: If histogram_freq is set and no validation data is provided.
 
-  @compatbility(eager)
+  @compatibility(eager)
   Using `Tensorboard` callback will work while eager execution is enabled,
   however outputting histogram summaries of weights and gradients is not
   supported, and thus `histogram_freq` will be ignored.

tensorflow/python/ops/losses/losses_impl.py

@@ -193,7 +193,7 @@ def compute_weighted_loss(
     gradient, you need to apply `tf.stop_gradient` to `weights` before
     passing them to `compute_weighted_loss`.
 
-  @compatbility(eager)
+  @compatibility(eager)
   The `loss_collection` argument is ignored when executing eagerly. Consider
   holding on to the return value or collecting losses via a `tf.keras.Model`.
   @end_compatibility
@@ -266,7 +266,7 @@ def absolute_difference(
       `labels` or if the shape of `weights` is invalid or if `labels`
       or `predictions` is None.
 
-  @compatbility(eager)
+  @compatibility(eager)
   The `loss_collection` argument is ignored when executing eagerly. Consider
   holding on to the return value or collecting losses via a `tf.keras.Model`.
   @end_compatibility
@@ -317,7 +317,7 @@ def cosine_distance(
     ValueError: If `predictions` shape doesn't match `labels` shape, or
       `axis`, `labels`, `predictions` or `weights` is `None`.
 
-  @compatbility(eager)
+  @compatibility(eager)
   The `loss_collection` argument is ignored when executing eagerly. Consider
   holding on to the return value or collecting losses via a `tf.keras.Model`.
   @end_compatibility
@@ -369,7 +369,7 @@ def hinge_loss(labels, logits, weights=1.0, scope=None,
     ValueError: If the shapes of `logits` and `labels` don't match or
       if `labels` or `logits` is None.
 
-  @compatbility(eager)
+  @compatibility(eager)
   The `loss_collection` argument is ignored when executing eagerly. Consider
   holding on to the return value or collecting losses via a `tf.keras.Model`.
   @end_compatibility
@@ -437,7 +437,7 @@ def huber_loss(labels, predictions, weights=1.0, delta=1.0, scope=None,
       if the shape of `weights` is invalid.  Also if `labels` or
      `predictions` is None.
 
-  @compatbility(eager)
+  @compatibility(eager)
   The `loss_collection` argument is ignored when executing eagerly. Consider
   holding on to the return value or collecting losses via a `tf.keras.Model`.
   @end_compatibility
@@ -503,7 +503,7 @@ def log_loss(labels, predictions, weights=1.0, epsilon=1e-7, scope=None,
       if the shape of `weights` is invalid.  Also if `labels` or `predictions`
       is None.
 
-  @compatbility(eager)
+  @compatibility(eager)
   The `loss_collection` argument is ignored when executing eagerly. Consider
   holding on to the return value or collecting losses via a `tf.keras.Model`.
   @end_compatibility
@@ -571,7 +571,7 @@ def mean_pairwise_squared_error(
       if the shape of `weights` is invalid.  Also if `labels` or `predictions`
       is None.
 
-  @compatbility(eager)
+  @compatibility(eager)
   The `loss_collection` argument is ignored when executing eagerly. Consider
   holding on to the return value or collecting losses via a `tf.keras.Model`.
   @end_compatibility
@@ -654,7 +654,7 @@ def mean_squared_error(
       if the shape of `weights` is invalid.  Also if `labels` or `predictions`
       is None.
 
-  @compatbility(eager)
+  @compatibility(eager)
   The `loss_collection` argument is ignored when executing eagerly. Consider
   holding on to the return value or collecting losses via a `tf.keras.Model`.
   @end_compatibility
@@ -711,7 +711,7 @@ def sigmoid_cross_entropy(
       `multi_class_labels` or if the shape of `weights` is invalid, or if
       `weights` is None.  Also if `multi_class_labels` or `logits` is None.
 
-  @compatbility(eager)
+  @compatibility(eager)
   The `loss_collection` argument is ignored when executing eagerly. Consider
   holding on to the return value or collecting losses via a `tf.keras.Model`.
   @end_compatibility
@@ -777,7 +777,7 @@ def softmax_cross_entropy(
       or if the shape of `weights` is invalid or if `weights` is None.  Also if
       `onehot_labels` or `logits` is None.
 
-  @compatbility(eager)
+  @compatibility(eager)
   The `loss_collection` argument is ignored when executing eagerly. Consider
   holding on to the return value or collecting losses via a `tf.keras.Model`.
   @end_compatibility
@@ -894,7 +894,7 @@ def sparse_softmax_cross_entropy(
     ValueError: If the shapes of `logits`, `labels`, and `weights` are
       incompatible, or if any of them are None.
 
-  @compatbility(eager)
+  @compatibility(eager)
   The `loss_collection` argument is ignored when executing eagerly. Consider
   holding on to the return value or collecting losses via a `tf.keras.Model`.
   @end_compatibility

tensorflow/python/summary/summary.py

@@ -268,7 +268,7 @@ def merge(inputs, collections=None, name=None):
   @compatibility(eager)
   Not compatible with eager execution. To write TensorBoard
   summaries under eager execution, use `tf.contrib.summary` instead.
-  @end_compatbility
+  @end_compatibility
   """
   # pylint: enable=line-too-long
   if _context.executing_eagerly():
@@ -304,7 +304,7 @@ def merge_all(key=_ops.GraphKeys.SUMMARIES, scope=None, name=None):
   @compatibility(eager)
   Not compatible with eager execution. To write TensorBoard
   summaries under eager execution, use `tf.contrib.summary` instead.
-  @end_compatbility
+  @end_compatibility
   """
   if _context.executing_eagerly():
     raise RuntimeError(
@@ -336,7 +336,7 @@ def get_summary_description(node_def):
   @compatibility(eager)
   Not compatible with eager execution. To write TensorBoard
   summaries under eager execution, use `tf.contrib.summary` instead.
-  @end_compatbility
+  @end_compatibility
   """
 
   if node_def.op != 'TensorSummary':

tensorflow/python/summary/writer/writer.py

@@ -352,7 +352,7 @@ def __init__(self,
     @compatibility(eager)
     `FileWriter` is not compatible with eager execution. To write TensorBoard
     summaries under eager execution, use `tf.contrib.summary` instead.
-    @end_compatbility
+    @end_compatibility
     """
     if context.executing_eagerly():
       raise RuntimeError(