Update generated Python Op docs.

Change: 147257715

tensorflow/g3doc/api_docs/python/contrib.distributions.md

@@ -15073,8 +15073,7 @@ arguments.
 The `event_shape` is given by the last dimension of `loc` or the last
 dimension of the matrix implied by `scale`.
 
-Recall that `covariance = scale @ scale.T`. A (non-batch) `scale` matrix
-is:
+Recall that `covariance = scale @ scale.T`. A (non-batch) `scale` matrix is:
 
 ```none
 scale = diag(scale_diag + scale_identity_multiplier * ones(k))
@@ -15095,7 +15094,7 @@ If both `scale_diag` and `scale_identity_multiplier` are `None`, then
 
 *  <b>`loc`</b>: Floating-point `Tensor`. If this is set to `None`, `loc` is
     implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where
-    `b >= 0` and `k` represents the event size.
+    `b >= 0` and `k` is the event size.
 *  <b>`scale_diag`</b>: Non-zero, floating-point `Tensor` representing a diagonal
     matrix added to `scale`. May have shape `[B1, ..., Bb, k]`, `b >= 0`,
     and characterizes `b`-batches of `k x k` diagonal matrices added to
@@ -15430,7 +15429,7 @@ Log of determinant of covariance matrix.
 Log probability density/mass function (depending on `is_continuous`).
 
 
-Additional documentation from `_MultivariateNormalLinearOperator`:
+Additional documentation from `MultivariateNormalLinearOperator`:
 
 `value` is a batch vector with compatible shape if `value` is a `Tensor` whose
 shape can be broadcast up to either:
@@ -15576,7 +15575,7 @@ Dictionary of parameters used to instantiate this `Distribution`.
 Probability density/mass function (depending on `is_continuous`).
 
 
-Additional documentation from `_MultivariateNormalLinearOperator`:
+Additional documentation from `MultivariateNormalLinearOperator`:
 
 `value` is a batch vector with compatible shape if `value` is a `Tensor` whose
 shape can be broadcast up to either:
@@ -15784,7 +15783,7 @@ X ~ MultivariateNormal(loc=0, scale=1)   # Identity scale, zero shift.
 Y = scale @ X + loc
 ```
 
-Trainable (batch) Cholesky matrices can be created with
+Trainable (batch) lower-triangular matrices can be created with
 `ds.matrix_diag_transform()` and/or `ds.fill_lower_triangular()`
 
 #### Examples
@@ -15843,8 +15842,7 @@ arguments.
 The `event_shape` is given by the last dimension of `loc` or the last
 dimension of the matrix implied by `scale`.
 
-Recall that `covariance = scale @ scale.T`. A (non-batch) `scale` matrix
-is:
+Recall that `covariance = scale @ scale.T`. A (non-batch) `scale` matrix is:
 
 ```none
 scale = scale_tril
@@ -16187,7 +16185,7 @@ Log of determinant of covariance matrix.
 Log probability density/mass function (depending on `is_continuous`).
 
 
-Additional documentation from `_MultivariateNormalLinearOperator`:
+Additional documentation from `MultivariateNormalLinearOperator`:
 
 `value` is a batch vector with compatible shape if `value` is a `Tensor` whose
 shape can be broadcast up to either:
@@ -16333,7 +16331,7 @@ Dictionary of parameters used to instantiate this `Distribution`.
 Probability density/mass function (depending on `is_continuous`).
 
 
-Additional documentation from `_MultivariateNormalLinearOperator`:
+Additional documentation from `MultivariateNormalLinearOperator`:
 
 `value` is a batch vector with compatible shape if `value` is a `Tensor` whose
 shape can be broadcast up to either:
@@ -16640,7 +16638,7 @@ If both `scale_diag` and `scale_identity_multiplier` are `None`, then
 
 *  <b>`loc`</b>: Floating-point `Tensor`. If this is set to `None`, `loc` is
     implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where
-    `b >= 0` and `k` represents the event size.
+    `b >= 0` and `k` is the event size.
 *  <b>`scale_diag`</b>: Non-zero, floating-point `Tensor` representing a diagonal
     matrix added to `scale`. May have shape `[B1, ..., Bb, k]`, `b >= 0`,
     and characterizes `b`-batches of `k x k` diagonal matrices added to
@@ -16985,7 +16983,7 @@ Log of determinant of covariance matrix.
 Log probability density/mass function (depending on `is_continuous`).
 
 
-Additional documentation from `_MultivariateNormalLinearOperator`:
+Additional documentation from `MultivariateNormalLinearOperator`:
 
 `value` is a batch vector with compatible shape if `value` is a `Tensor` whose
 shape can be broadcast up to either:
@@ -17131,7 +17129,7 @@ Dictionary of parameters used to instantiate this `Distribution`.
 Probability density/mass function (depending on `is_continuous`).
 
 
-Additional documentation from `_MultivariateNormalLinearOperator`:
+Additional documentation from `MultivariateNormalLinearOperator`:
 
 `value` is a batch vector with compatible shape if `value` is a `Tensor` whose
 shape can be broadcast up to either:
@@ -17610,7 +17608,7 @@ Log of determinant of covariance matrix.
 Log probability density/mass function (depending on `is_continuous`).
 
 
-Additional documentation from `_MultivariateNormalLinearOperator`:
+Additional documentation from `MultivariateNormalLinearOperator`:
 
 `value` is a batch vector with compatible shape if `value` is a `Tensor` whose
 shape can be broadcast up to either:
@@ -17756,7 +17754,7 @@ Dictionary of parameters used to instantiate this `Distribution`.
 Probability density/mass function (depending on `is_continuous`).
 
 
-Additional documentation from `_MultivariateNormalLinearOperator`:
+Additional documentation from `MultivariateNormalLinearOperator`:
 
 `value` is a batch vector with compatible shape if `value` is a `Tensor` whose
 shape can be broadcast up to either:

tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiag.md

@@ -109,8 +109,7 @@ arguments.
 The `event_shape` is given by the last dimension of `loc` or the last
 dimension of the matrix implied by `scale`.
 
-Recall that `covariance = scale @ scale.T`. A (non-batch) `scale` matrix
-is:
+Recall that `covariance = scale @ scale.T`. A (non-batch) `scale` matrix is:
 
 ```none
 scale = diag(scale_diag + scale_identity_multiplier * ones(k))
@@ -131,7 +130,7 @@ If both `scale_diag` and `scale_identity_multiplier` are `None`, then
 
 *  <b>`loc`</b>: Floating-point `Tensor`. If this is set to `None`, `loc` is
     implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where
-    `b >= 0` and `k` represents the event size.
+    `b >= 0` and `k` is the event size.
 *  <b>`scale_diag`</b>: Non-zero, floating-point `Tensor` representing a diagonal
     matrix added to `scale`. May have shape `[B1, ..., Bb, k]`, `b >= 0`,
     and characterizes `b`-batches of `k x k` diagonal matrices added to
@@ -466,7 +465,7 @@ Log of determinant of covariance matrix.
 Log probability density/mass function (depending on `is_continuous`).
 
 
-Additional documentation from `_MultivariateNormalLinearOperator`:
+Additional documentation from `MultivariateNormalLinearOperator`:
 
 `value` is a batch vector with compatible shape if `value` is a `Tensor` whose
 shape can be broadcast up to either:
@@ -612,7 +611,7 @@ Dictionary of parameters used to instantiate this `Distribution`.
 Probability density/mass function (depending on `is_continuous`).
 
 
-Additional documentation from `_MultivariateNormalLinearOperator`:
+Additional documentation from `MultivariateNormalLinearOperator`:
 
 `value` is a batch vector with compatible shape if `value` is a `Tensor` whose
 shape can be broadcast up to either:

tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiagWithSoftplusScale.md

@@ -313,7 +313,7 @@ Log of determinant of covariance matrix.
 Log probability density/mass function (depending on `is_continuous`).
 
 
-Additional documentation from `_MultivariateNormalLinearOperator`:
+Additional documentation from `MultivariateNormalLinearOperator`:
 
 `value` is a batch vector with compatible shape if `value` is a `Tensor` whose
 shape can be broadcast up to either:
@@ -459,7 +459,7 @@ Dictionary of parameters used to instantiate this `Distribution`.
 Probability density/mass function (depending on `is_continuous`).
 
 
-Additional documentation from `_MultivariateNormalLinearOperator`:
+Additional documentation from `MultivariateNormalLinearOperator`:
 
 `value` is a batch vector with compatible shape if `value` is a `Tensor` whose
 shape can be broadcast up to either:

tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalDiagPlusLowRank.md

@@ -141,7 +141,7 @@ If both `scale_diag` and `scale_identity_multiplier` are `None`, then
 
 *  <b>`loc`</b>: Floating-point `Tensor`. If this is set to `None`, `loc` is
     implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where
-    `b >= 0` and `k` represents the event size.
+    `b >= 0` and `k` is the event size.
 *  <b>`scale_diag`</b>: Non-zero, floating-point `Tensor` representing a diagonal
     matrix added to `scale`. May have shape `[B1, ..., Bb, k]`, `b >= 0`,
     and characterizes `b`-batches of `k x k` diagonal matrices added to
@@ -486,7 +486,7 @@ Log of determinant of covariance matrix.
 Log probability density/mass function (depending on `is_continuous`).
 
 
-Additional documentation from `_MultivariateNormalLinearOperator`:
+Additional documentation from `MultivariateNormalLinearOperator`:
 
 `value` is a batch vector with compatible shape if `value` is a `Tensor` whose
 shape can be broadcast up to either:
@@ -632,7 +632,7 @@ Dictionary of parameters used to instantiate this `Distribution`.
 Probability density/mass function (depending on `is_continuous`).
 
 
-Additional documentation from `_MultivariateNormalLinearOperator`:
+Additional documentation from `MultivariateNormalLinearOperator`:
 
 `value` is a batch vector with compatible shape if `value` is a `Tensor` whose
 shape can be broadcast up to either:

tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.MultivariateNormalTriL.md

@@ -42,7 +42,7 @@ X ~ MultivariateNormal(loc=0, scale=1)   # Identity scale, zero shift.
 Y = scale @ X + loc
 ```
 
-Trainable (batch) Cholesky matrices can be created with
+Trainable (batch) lower-triangular matrices can be created with
 `ds.matrix_diag_transform()` and/or `ds.fill_lower_triangular()`
 
 #### Examples
@@ -101,8 +101,7 @@ arguments.
 The `event_shape` is given by the last dimension of `loc` or the last
 dimension of the matrix implied by `scale`.
 
-Recall that `covariance = scale @ scale.T`. A (non-batch) `scale` matrix
-is:
+Recall that `covariance = scale @ scale.T`. A (non-batch) `scale` matrix is:
 
 ```none
 scale = scale_tril
@@ -445,7 +444,7 @@ Log of determinant of covariance matrix.
 Log probability density/mass function (depending on `is_continuous`).
 
 
-Additional documentation from `_MultivariateNormalLinearOperator`:
+Additional documentation from `MultivariateNormalLinearOperator`:
 
 `value` is a batch vector with compatible shape if `value` is a `Tensor` whose
 shape can be broadcast up to either:
@@ -591,7 +590,7 @@ Dictionary of parameters used to instantiate this `Distribution`.
 Probability density/mass function (depending on `is_continuous`).
 
 
-Additional documentation from `_MultivariateNormalLinearOperator`:
+Additional documentation from `MultivariateNormalLinearOperator`:
 
 `value` is a batch vector with compatible shape if `value` is a `Tensor` whose
 shape can be broadcast up to either: