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diff --git a/tensorflow/core/api_def/base_api/api_def_CudnnRNNParamsToCanonical.pbtxt b/tensorflow/core/api_def/base_api/api_def_CudnnRNNParamsToCanonical.pbtxt
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+op {
+  graph_op_name: "CudnnRNNParamsToCanonical"
+  summary: "Retrieves CudnnRNN params in canonical form."
+  description: <<END
+Retrieves a set of weights from the opaque params buffer that can be saved and
+restored in a way compatible with future runs.
+
+Note that the params buffer may not be compatible across different GPUs. So any
+save and restoration should be converted to and from the canonical weights and
+biases.
+
+num_layers: Specifies the number of layers in the RNN model.
+num_units: Specifies the size of the hidden state.
+input_size: Specifies the size of the input state.
+num_params: number of parameter sets for all layers.
+    Each layer may contain multiple parameter sets, with each set consisting of
+    a weight matrix and a bias vector.
+weights: the canonical form of weights that can be used for saving
+    and restoration. They are more likely to be compatible across different
+    generations.
+biases: the canonical form of biases that can be used for saving
+    and restoration. They are more likely to be compatible across different
+    generations.
+rnn_mode: Indicates the type of the RNN model.
+input_mode: Indicate whether there is a linear projection between the input and
+    The actual computation before the first layer. 'skip_input' is only allowed
+    when input_size == num_units; 'auto_select' implies 'skip_input' when
+    input_size == num_units; otherwise, it implies 'linear_input'.
+direction: Indicates whether a bidirectional model will be used.
+    dir = (direction == bidirectional) ? 2 : 1
+dropout: dropout probability. When set to 0., dropout is disabled.
+seed: the 1st part of a seed to initialize dropout.
+seed2: the 2nd part of a seed to initialize dropout.
+END
+}