Automated g4 rollback of changelist 180691955

PiperOrigin-RevId: 181365803

1 files changed, 10 insertions, 180 deletions

diff --git a/tensorflow/core/ops/random_ops.cc b/tensorflow/core/ops/random_ops.cc
index 31d9c82e53..f6c668f5c9 100644
--- a/tensorflow/core/ops/random_ops.cc
+++ b/tensorflow/core/ops/random_ops.cc
@@ -31,22 +31,7 @@ REGISTER_OP("RandomUniform")
     .Attr("seed2: int = 0")
     .Attr("dtype: {half,bfloat16,float,double}")
     .Attr("T: {int32, int64}")
-    .SetShapeFn(shape_inference::RandomShape)
-    .Doc(R"doc(
-Outputs random values from a uniform distribution.
-
-The generated values follow a uniform distribution in the range `[0, 1)`. The
-lower bound 0 is included in the range, while the upper bound 1 is excluded.
-
-shape: The shape of the output tensor.
-dtype: The type of the output.
-seed: If either `seed` or `seed2` are set to be non-zero, the random number
-  generator is seeded by the given seed.  Otherwise, it is seeded by a
-  random seed.
-seed2: A second seed to avoid seed collision.
-
-output: A tensor of the specified shape filled with uniform random values.
-)doc");
+    .SetShapeFn(shape_inference::RandomShape);
 
 REGISTER_OP("RandomUniformInt")
     .Input("shape: T")
@@ -58,28 +43,7 @@ REGISTER_OP("RandomUniformInt")
     .Attr("seed2: int = 0")
     .Attr("Tout: {int32, int64}")
     .Attr("T: {int32, int64}")
-    .SetShapeFn(shape_inference::RandomShape)
-    .Doc(R"doc(
-Outputs random integers from a uniform distribution.
-
-The generated values are uniform integers in the range `[minval, maxval)`.
-The lower bound `minval` is included in the range, while the upper bound
-`maxval` is excluded.
-
-The random integers are slightly biased unless `maxval - minval` is an exact
-power of two.  The bias is small for values of `maxval - minval` significantly
-smaller than the range of the output (either `2^32` or `2^64`).
-
-shape: The shape of the output tensor.
-minval: 0-D.  Inclusive lower bound on the generated integers.
-maxval: 0-D.  Exclusive upper bound on the generated integers.
-seed: If either `seed` or `seed2` are set to be non-zero, the random number
-  generator is seeded by the given seed.  Otherwise, it is seeded by a
-  random seed.
-seed2: A second seed to avoid seed collision.
-
-output: A tensor of the specified shape filled with uniform random integers.
-)doc");
+    .SetShapeFn(shape_inference::RandomShape);
 
 REGISTER_OP("RandomStandardNormal")
     .Input("shape: T")
@@ -89,21 +53,7 @@ REGISTER_OP("RandomStandardNormal")
     .Attr("seed2: int = 0")
     .Attr("dtype: {half,bfloat16,float,double}")
     .Attr("T: {int32, int64}")
-    .SetShapeFn(shape_inference::RandomShape)
-    .Doc(R"doc(
-Outputs random values from a normal distribution.
-
-The generated values will have mean 0 and standard deviation 1.
-
-shape: The shape of the output tensor.
-dtype: The type of the output.
-seed: If either `seed` or `seed2` are set to be non-zero, the random number
-  generator is seeded by the given seed.  Otherwise, it is seeded by a
-  random seed.
-seed2: A second seed to avoid seed collision.
-
-output: A tensor of the specified shape filled with random normal values.
-)doc");
+    .SetShapeFn(shape_inference::RandomShape);
 
 REGISTER_OP("ParameterizedTruncatedNormal")
     .Input("shape: T")
@@ -117,27 +67,7 @@ REGISTER_OP("ParameterizedTruncatedNormal")
     .Attr("seed2: int = 0")
     .Attr("dtype: {half,bfloat16,float,double}")
     .Attr("T: {int32, int64}")
-    .SetShapeFn(shape_inference::RandomShape)
-    .Doc(R"doc(
-Outputs random values from a normal distribution. The parameters may each be a
-scalar which applies to the entire output, or a vector of length shape[0] which
-stores the parameters for each batch.
-
-shape: The shape of the output tensor. Batches are indexed by the 0th dimension.
-means: The mean parameter of each batch.
-stdevs: The standard deviation parameter of each batch. Must be greater than 0.
-minvals: The minimum cutoff. May be -infinity.
-maxvals: The maximum cutoff. May be +infinity, and must be more than the minval
-  for each batch.
-dtype: The type of the output.
-seed: If either `seed` or `seed2` are set to be non-zero, the random number
-  generator is seeded by the given seed.  Otherwise, it is seeded by a
-  random seed.
-seed2: A second seed to avoid seed collision.
-
-output: A matrix of shape num_batches x samples_per_batch, filled with random
-  truncated normal values using the parameters for each row.
-)doc");
+    .SetShapeFn(shape_inference::RandomShape);
 
 REGISTER_OP("TruncatedNormal")
     .Input("shape: T")
@@ -147,24 +77,7 @@ REGISTER_OP("TruncatedNormal")
     .Attr("seed2: int = 0")
     .Attr("dtype: {half,bfloat16,float,double}")
     .Attr("T: {int32, int64}")
-    .SetShapeFn(shape_inference::RandomShape)
-    .Doc(R"doc(
-Outputs random values from a truncated normal distribution.
-
-The generated values follow a normal distribution with mean 0 and standard
-deviation 1, except that values whose magnitude is more than 2 standard
-deviations from the mean are dropped and re-picked.
-
-shape: The shape of the output tensor.
-dtype: The type of the output.
-seed: If either `seed` or `seed2` are set to be non-zero, the random number
-  generator is seeded by the given seed.  Otherwise, it is seeded by a
-  random seed.
-seed2: A second seed to avoid seed collision.
-
-output: A tensor of the specified shape filled with random truncated normal
-  values.
-)doc");
+    .SetShapeFn(shape_inference::RandomShape);
 
 REGISTER_OP("RandomShuffle")
     .Input("value: T")
@@ -173,29 +86,7 @@ REGISTER_OP("RandomShuffle")
     .Attr("seed: int = 0")
     .Attr("seed2: int = 0")
     .Attr("T: type")
-    .SetShapeFn(shape_inference::UnchangedShape)
-    .Doc(R"doc(
-Randomly shuffles a tensor along its first dimension.
-
-  The tensor is shuffled along dimension 0, such that each `value[j]` is mapped
-  to one and only one `output[i]`. For example, a mapping that might occur for a
-  3x2 tensor is:
-
-```
-[[1, 2],       [[5, 6],
- [3, 4],  ==>   [1, 2],
- [5, 6]]        [3, 4]]
-```
-
-value: The tensor to be shuffled.
-seed: If either `seed` or `seed2` are set to be non-zero, the random number
-  generator is seeded by the given seed.  Otherwise, it is seeded by a
-  random seed.
-seed2: A second seed to avoid seed collision.
-
-output: A tensor of same shape and type as `value`, shuffled along its first
-  dimension.
-)doc");
+    .SetShapeFn(shape_inference::UnchangedShape);
 
 REGISTER_OP("Multinomial")
     .SetIsStateful()
@@ -215,19 +106,7 @@ REGISTER_OP("Multinomial")
       TF_RETURN_IF_ERROR(c->MakeDimForScalarInput(1, &num_samples));
       c->set_output(0, c->Matrix(c->Dim(logits_shape, 0), num_samples));
       return Status::OK();
-    })
-    .Doc(R"doc(
-Draws samples from a multinomial distribution.
-
-logits: 2-D Tensor with shape `[batch_size, num_classes]`.  Each slice `[i, :]`
-  represents the unnormalized log probabilities for all classes.
-num_samples: 0-D.  Number of independent samples to draw for each row slice.
-seed: If either seed or seed2 is set to be non-zero, the internal random number
-  generator is seeded by the given seed.  Otherwise, a random seed is used.
-seed2: A second seed to avoid seed collision.
-output: 2-D Tensor with shape `[batch_size, num_samples]`.  Each slice `[i, :]`
-  contains the drawn class labels with range `[0, num_classes)`.
-)doc");
+    });
 
 REGISTER_OP("RandomGamma")
     .SetIsStateful()
@@ -244,27 +123,7 @@ REGISTER_OP("RandomGamma")
       TF_RETURN_IF_ERROR(c->Concatenate(out, c->input(1), &out));
       c->set_output(0, out);
       return Status::OK();
-    })
-    .Doc(R"doc(
-Outputs random values from the Gamma distribution(s) described by alpha.
-
-This op uses the algorithm by Marsaglia et al. to acquire samples via
-transformation-rejection from pairs of uniform and normal random variables.
-See http://dl.acm.org/citation.cfm?id=358414
-
-shape: 1-D integer tensor. Shape of independent samples to draw from each
-  distribution described by the shape parameters given in alpha.
-alpha: A tensor in which each scalar is a "shape" parameter describing the
-  associated gamma distribution.
-seed: If either `seed` or `seed2` are set to be non-zero, the random number
-  generator is seeded by the given seed.  Otherwise, it is seeded by a
-  random seed.
-seed2: A second seed to avoid seed collision.
-
-output: A tensor with shape `shape + shape(alpha)`. Each slice
-  `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for
-  `alpha[i0, i1, ...iN]`. The dtype of the output matches the dtype of alpha.
-)doc");
+    });
 
 REGISTER_OP("RandomPoisson")
     .SetIsStateful()
@@ -282,10 +141,7 @@ REGISTER_OP("RandomPoisson")
       c->set_output(0, out);
       return Status::OK();
     })
-    .Deprecated(25, "Replaced by RandomPoissonV2")
-    .Doc(R"doc(
-Use RandomPoissonV2 instead.
-)doc");
+    .Deprecated(25, "Replaced by RandomPoissonV2");
 
 REGISTER_OP("RandomPoissonV2")
     .SetIsStateful()
@@ -303,32 +159,6 @@ REGISTER_OP("RandomPoissonV2")
       TF_RETURN_IF_ERROR(c->Concatenate(out, c->input(1), &out));
       c->set_output(0, out);
       return Status::OK();
-    })
-    .Doc(R"doc(
-Outputs random values from the Poisson distribution(s) described by rate.
-
-This op uses two algorithms, depending on rate. If rate >= 10, then
-the algorithm by Hormann is used to acquire samples via
-transformation-rejection.
-See http://www.sciencedirect.com/science/article/pii/0167668793909974.
-
-Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform
-random variables.
-See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer
-Programming, Volume 2. Addison Wesley
-
-shape: 1-D integer tensor. Shape of independent samples to draw from each
-  distribution described by the shape parameters given in rate.
-rate: A tensor in which each scalar is a "rate" parameter describing the
-  associated poisson distribution.
-seed: If either `seed` or `seed2` are set to be non-zero, the random number
-  generator is seeded by the given seed.  Otherwise, it is seeded by a
-  random seed.
-seed2: A second seed to avoid seed collision.
-
-output: A tensor with shape `shape + shape(rate)`. Each slice
-  `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for
-  `rate[i0, i1, ...iN]`.
-)doc");
+    });
 
 }  // namespace tensorflow