Remove usage of magic-api-link syntax from source files.

Back-ticks are now converted to links in the api_docs generator. With the new docs repo we're moving to simplify the docs pipeline, and make everything more readable.

By doing this we no longer get test failures for symbols that don't exist (`tf.does_not_exist`  will not get a link).

There is also no way, not to set custom link text. That's okay.

This is the result of the following regex replacement (+ a couple of manual edits.):

re: @\{([^$].*?)(\$.+?)?}
sub: `\1`

Which does the following replacements:

"@{tf.symbol}" --> "`tf.symbol`"
"@{tf.symbol$link_text}" --> "`tf.symbol`"

PiperOrigin-RevId: 208042358

1 files changed, 35 insertions, 35 deletions

diff --git a/tensorflow/contrib/all_reduce/python/all_reduce.py b/tensorflow/contrib/all_reduce/python/all_reduce.py
index 159d985db5..3b539734a2 100644
--- a/tensorflow/contrib/all_reduce/python/all_reduce.py
+++ b/tensorflow/contrib/all_reduce/python/all_reduce.py
@@ -32,10 +32,10 @@ def _flatten_tensors(tensors):
   """Check tensors for isomorphism and flatten.
 
   Args:
-    tensors: list of T @{tf.Tensor} which must all have the same shape.
+    tensors: list of T `tf.Tensor` which must all have the same shape.
 
   Returns:
-    tensors: a list of T @{tf.Tensor} which are flattened (1D) views of tensors
+    tensors: a list of T `tf.Tensor` which are flattened (1D) views of tensors
     shape: the original shape of each element of input tensors
 
   Raises:
@@ -61,12 +61,12 @@ def _reshape_tensors(tensors, shape):
   """Reshape tensors flattened by _flatten_tensors.
 
   Args:
-    tensors: list of T @{tf.Tensor} of identical length 1D tensors.
+    tensors: list of T `tf.Tensor` of identical length 1D tensors.
     shape: list of integers describing the desired shape.  Product of
       the elements must equal the length of each tensor.
 
   Returns:
-    list of T @{tf.Tensor} which are the reshaped inputs.
+    list of T `tf.Tensor` which are the reshaped inputs.
   """
   reshaped = []
   for t in tensors:
@@ -79,12 +79,12 @@ def _padded_split(tensor, pieces):
   """Like split for 1D tensors but pads-out case where len % pieces != 0.
 
   Args:
-    tensor: T @{tf.Tensor} that must be 1D.
+    tensor: T `tf.Tensor` that must be 1D.
     pieces: a positive integer specifying the number of pieces into which
       tensor should be split.
 
   Returns:
-    list of T @{tf.Tensor} of length pieces, which hold the values of
+    list of T `tf.Tensor` of length pieces, which hold the values of
       thin input tensor, in order.  The final tensor may
       be zero-padded on the end to make its size equal to those of all
       of the other tensors.
@@ -132,11 +132,11 @@ def _strip_padding(tensors, pad_len):
   """Strip the suffix padding added by _padded_split.
 
   Args:
-    tensors: list of T @{tf.Tensor} of identical length 1D tensors.
+    tensors: list of T `tf.Tensor` of identical length 1D tensors.
     pad_len: number of elements to be stripped from the end of each tensor.
 
   Returns:
-    list of T @{tf.Tensor} which are the stripped inputs.
+    list of T `tf.Tensor` which are the stripped inputs.
 
   Raises:
     ValueError: tensors must be a non-empty list of 1D tensors, and
@@ -161,12 +161,12 @@ def _ragged_split(tensor, pieces):
   """Like split for 1D tensors but allows case where len % pieces != 0.
 
   Args:
-    tensor: T @{tf.Tensor} that must be 1D.
+    tensor: T `tf.Tensor` that must be 1D.
     pieces: a positive integer specifying the number of pieces into which
       tensor should be split.
 
   Returns:
-    list of T @{tf.Tensor} of length pieces, which hold the values of
+    list of T `tf.Tensor` of length pieces, which hold the values of
       the input tensor, in order.  The final tensor may be shorter
       than the others, which will all be of equal length.
 
@@ -256,7 +256,7 @@ def build_ring_all_reduce(input_tensors, num_workers, num_subchunks,
   """Construct a subgraph performing a ring-style all-reduce of input_tensors.
 
   Args:
-    input_tensors: a list of T @{tf.Tensor} objects, which must all
+    input_tensors: a list of T `tf.Tensor` objects, which must all
       have the same shape and type.
     num_workers: number of worker tasks spanned by input_tensors.
     num_subchunks: number of subchunks each device should process in one tick.
@@ -272,7 +272,7 @@ def build_ring_all_reduce(input_tensors, num_workers, num_subchunks,
     size.
 
   Returns:
-    a list of T @{tf.Tensor} identical sum-reductions of input_tensors.
+    a list of T `tf.Tensor` identical sum-reductions of input_tensors.
   """
   if len(input_tensors) < 2:
     raise ValueError("input_tensors must be length 2 or longer")
@@ -299,7 +299,7 @@ def _build_ring_gather(input_tensors, devices, num_subchunks,
   """Construct a subgraph for the first (reduction) pass of ring all-reduce.
 
   Args:
-    input_tensors: a list of T @{tf.Tensor} 1D input tensors of same
+    input_tensors: a list of T `tf.Tensor` 1D input tensors of same
       shape and type.
     devices: array of device name strings
     num_subchunks: number of subchunks each device should process in one tick.
@@ -311,7 +311,7 @@ def _build_ring_gather(input_tensors, devices, num_subchunks,
     ValueError: tensors must all be one dimensional.
 
   Returns:
-    list of list of T @{tf.Tensor} of (partially) reduced values where
+    list of list of T `tf.Tensor` of (partially) reduced values where
     exactly num_subchunks chunks at each device are fully reduced.
   """
   num_devices = len(input_tensors)
@@ -360,11 +360,11 @@ def _apply_unary_to_chunks(f, chunks_by_dev):
   """Apply a unary op to each tensor in chunks_by_dev, on same device.
 
   Args:
-    f: a unary function over T @{tf.Tensor}.
-    chunks_by_dev: list of lists of T @{tf.Tensor}.
+    f: a unary function over T `tf.Tensor`.
+    chunks_by_dev: list of lists of T `tf.Tensor`.
 
   Returns:
-    new list of lists of T @{tf.Tensor} with the same structure as
+    new list of lists of T `tf.Tensor` with the same structure as
     chunks_by_dev containing the derived tensors.
   """
   output = []
@@ -381,14 +381,14 @@ def _build_ring_scatter(pred_by_s_d, rank_by_s_d,
   Args:
     pred_by_s_d: as produced by _ring_permutations
     rank_by_s_d: as produced by _ring_permutations
-    chunks_by_dev: list of list of T @{tf.Tensor} indexed by ints
+    chunks_by_dev: list of list of T `tf.Tensor` indexed by ints
       (device, chunk)
 
   Raises:
     ValueError: chunks_by_dev is not well-formed
 
   Returns:
-    list of T @{tf.Tensor} which are the fully reduced tensors, one
+    list of T `tf.Tensor` which are the fully reduced tensors, one
     at each device corresponding to the outer dimension of chunks_by_dev.
   """
   num_devices = len(chunks_by_dev)
@@ -448,12 +448,12 @@ def build_recursive_hd_all_reduce(input_tensors, red_op, un_op=None):
     the future with edge-case specific logic.
 
   Args:
-    input_tensors: list of T @{tf.Tensor} to be elementwise reduced.
+    input_tensors: list of T `tf.Tensor` to be elementwise reduced.
     red_op: a binary elementwise reduction Op.
     un_op: an optional unary elementwise Op to apply to reduced values.
 
   Returns:
-    list of T @{tf.Tensor} which are the fully reduced tensors, one
+    list of T `tf.Tensor` which are the fully reduced tensors, one
     at each device of input_tensors.
 
   Raises:
@@ -475,13 +475,13 @@ def _build_recursive_hd_gather(input_tensors, devices, red_op):
   """Construct the gather phase of recursive halving-doubling all-reduce.
 
   Args:
-    input_tensors: list of T @{tf.Tensor} to be elementwise reduced.
+    input_tensors: list of T `tf.Tensor` to be elementwise reduced.
     devices: a list of strings naming the devices hosting input_tensors,
       which will also be used to host the (partial) reduction values.
     red_op: a binary elementwise reduction Op.
 
   Returns:
-    list of T @{tf.Tensor} which are the fully reduced tensor shards.
+    list of T `tf.Tensor` which are the fully reduced tensor shards.
 
   Raises:
     ValueError: num_devices not a power of 2, or tensor len not divisible
@@ -516,12 +516,12 @@ def _build_recursive_hd_scatter(input_tensors, devices):
   """Construct the scatter phase of recursive halving-doublng all-reduce.
 
   Args:
-    input_tensors: list of T @{tf.Tensor} that are fully-reduced shards.
+    input_tensors: list of T `tf.Tensor` that are fully-reduced shards.
     devices: a list of strings naming the devices on which the reconstituted
       full tensors should be placed.
 
   Returns:
-    list of T @{tf.Tensor} which are the fully reduced tensors.
+    list of T `tf.Tensor` which are the fully reduced tensors.
   """
   num_devices = len(devices)
   num_hops = int(math.log(num_devices, 2))
@@ -571,7 +571,7 @@ def build_shuffle_all_reduce(input_tensors, gather_devices, red_op, un_op=None):
     un_op: optional elementwise unary Op to be applied to fully-reduced values.
 
   Returns:
-    list of T @{tf.Tensor} which are the fully reduced tensors.
+    list of T `tf.Tensor` which are the fully reduced tensors.
   """
   input_tensors, shape = _flatten_tensors(input_tensors)
   dst_devices = [t.device for t in input_tensors]
@@ -594,7 +594,7 @@ def _build_shuffle_gather(input_tensors, gather_devices, red_op, un_op=None):
     un_op: optional elementwise unary Op to be applied to fully-reduced values.
 
   Returns:
-    list of T @{tf.Tensor} which are the fully reduced shards.
+    list of T `tf.Tensor` which are the fully reduced shards.
 
   Raises:
     ValueError: inputs not well-formed.
@@ -629,7 +629,7 @@ def _build_shuffle_scatter(reduced_shards, dst_devices):
       should be reconstituted.
 
   Returns:
-    list of T @{tf.Tensor} scattered tensors.
+    list of T `tf.Tensor` scattered tensors.
   """
   num_devices = len(dst_devices)
   out_tensors = []
@@ -644,7 +644,7 @@ def _split_by_task(devices, values):
 
   Args:
     devices: list of device name strings
-    values: list of T @{tf.tensor} of same length as devices.
+    values: list of T `tf.tensor` of same length as devices.
 
   Returns:
     (per_task_devices, per_task_values) where both values are
@@ -680,14 +680,14 @@ def build_nccl_all_reduce(input_tensors, red_op, un_op=None):
   """Build a subgraph that does one full all-reduce, using NCCL.
 
   Args:
-    input_tensors: list of T @{tf.Tensor} of same-shape and type values to
+    input_tensors: list of T `tf.Tensor` of same-shape and type values to
       be reduced.
     red_op: binary elementwise reduction operator.  Must be one of
       {tf.add}
     un_op: optional unary elementwise Op to apply to fully-reduce values.
 
   Returns:
-    list of T @{tf.Tensor} of reduced values.
+    list of T `tf.Tensor` of reduced values.
 
   Raises:
     ValueError: red_op not supported.
@@ -709,14 +709,14 @@ def _build_nccl_hybrid(input_tensors, red_op, upper_level_f):
   """Construct a subgraph for NCCL hybrid all-reduce.
 
   Args:
-    input_tensors: list of T @{tf.Tensor} of same-shape and type values to
+    input_tensors: list of T `tf.Tensor` of same-shape and type values to
       be reduced.
     red_op: binary elementwise reduction operator.
     upper_level_f: function for reducing one value per worker, across
       workers.
 
   Returns:
-    list of T @{tf.Tensor} of reduced values.
+    list of T `tf.Tensor` of reduced values.
 
   Raises:
     ValueError: inputs not well-formed.
@@ -797,7 +797,7 @@ def _build_shuffle_hybrid(input_tensors, gather_devices, red_op, upper_level_f):
   """Construct a subgraph for Shuffle hybrid all-reduce.
 
   Args:
-    input_tensors: list of T @{tf.Tensor} of same-shape and type values to
+    input_tensors: list of T `tf.Tensor` of same-shape and type values to
       be reduced.
     gather_devices: list of device names on which to host gather shards.
     red_op: binary elementwise reduction operator.
@@ -805,7 +805,7 @@ def _build_shuffle_hybrid(input_tensors, gather_devices, red_op, upper_level_f):
       workers.
 
   Returns:
-    list of T @{tf.Tensor} of reduced values.
+    list of T `tf.Tensor` of reduced values.
 
   Raises:
     ValueError: inputs not well-formed.