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+### `tf.nn.top_k(input, k=1, sorted=True, name=None)` {#top_k}
+
+Finds values and indices of the `k` largest entries for the last dimension.
+
+If the input is a vector (rank-1), finds the `k` largest entries in the vector
+and outputs their values and indices as vectors.  Thus `values[j]` is the
+`j`-th largest entry in `input`, and its index is `indices[j]`.
+
+For matrices (resp. higher rank input), computes the top `k` entries in each
+row (resp. vector along the last dimension).  Thus,
+
+    values.shape = indices.shape = input.shape[:-1] + [k]
+
+If two elements are equal, the lower-index element appears first.
+
+##### Args:
+
+
+*  <b>`input`</b>: 1-D or higher `Tensor` with last dimension at least `k`.
+*  <b>`k`</b>: 0-D `int32` `Tensor`.  Number of top elements to look for along the last
+    dimension (along each row for matrices).
+*  <b>`sorted`</b>: If true the resulting `k` elements will be sorted by the values in
+    descending order.
+*  <b>`name`</b>: Optional name for the operation.
+
+##### Returns:
+
+
+*  <b>`values`</b>: The `k` largest elements along each last dimensional slice.
+*  <b>`indices`</b>: The indices of `values` within the last dimension of `input`.
+