Update generated Python Op docs.

Change: 126958138

tensorflow/g3doc/api_docs/python/array_ops.md

@@ -578,6 +578,17 @@ split0, split1, split2 = tf.split(1, 3, value)
 tf.shape(split0) ==> [5, 10]
 ```
 
+Note: If you are splitting along an axis by the length of that axis, consider
+using unpack, e.g.
+```python
+num_items = t.get_shape()[axis].value
+[tf.squeeze(s, [axis]) for s in tf.split(axis, num_items, t)]
+```
+can be rewritten as
+```python
+tf.unpack(t, axis=axis)
+```
+
 ##### Args:
 
 
@@ -713,6 +724,16 @@ tf.shape(tf.concat(0, [t3, t4])) ==> [4, 3]
 tf.shape(tf.concat(1, [t3, t4])) ==> [2, 6]
 ```
 
+Note: If you are concatenating along a new axis consider using pack.
+E.g.
+```python
+tf.concat(axis, [tf.expand_dims(t, axis) for t in ts])
+```
+can be rewritten as
+```
+tf.pack(tensors, axis=axis)
+```
+
 ##### Args:
 
 
@@ -731,9 +752,23 @@ tf.shape(tf.concat(1, [t3, t4])) ==> [2, 6]
 
 Packs a list of rank-`R` tensors into one rank-`(R+1)` tensor.
 
-Packs tensors in `values` into a tensor with rank one higher than each tensor
-in `values` and shape `[len(values)] + values[0].shape`. The output satisfies
-`output[i, ...] = values[i][...]`.
+Packs the list of tensors in `values` into a tensor with rank one higher than
+each tensor in `values`, by packing them along the `axis` dimension.
+Given a list of length `N` of tensors of shape `(A, B, C)`;
+
+if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.
+if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.
+Etc.
+
+For example:
+
+```prettyprint
+# 'x' is [1, 4]
+# 'y' is [2, 5]
+# 'z' is [3, 6]
+pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]]  # Pack along first dim.
+pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
+```
 
 This is the opposite of unpack.  The numpy equivalent is
 
@@ -764,12 +799,19 @@ This is the opposite of unpack.  The numpy equivalent is
 
 Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
 
-Unpacks `num` tensors from `value` along the given dimension.
+Unpacks `num` tensors from `value` by chipping it along the `axis` dimension.
 If `num` is not specified (the default), it is inferred from `value`'s shape.
 If `value.shape[axis]` is not known, `ValueError` is raised.
 
-The ith tensor in `output` is the slice `value[i, ...]`. Each tensor in
-`output` has shape `value.shape[1:]`.
+For example, given a tensor of shape `(A, B, C, D)`;
+
+If `axis == 0` then the i'th tensor in `output` is the slice
+  `value[i, :, :, :]` and each tensor in `output` will have shape `(B, C, D)`.
+  (Note that the dimension unpacked along is gone, unlike `split`).
+
+If `axis == 1` then the i'th tensor in `output` is the slice
+  `value[:, i, :, :]` and each tensor in `output` will have shape `(A, C, D)`.
+Etc.
 
 This is the opposite of pack.  The numpy equivalent is
 

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

@@ -1982,6 +1982,340 @@ Variance of each batch member.
 
 
 
+- - -
+
+### `class tf.contrib.distributions.Laplace` {#Laplace}
+
+The Laplace distribution with location and scale > 0 parameters.
+
+#### Mathematical details
+
+The PDF of this distribution is:
+
+```f(x | mu, b, b > 0) = 0.5 / b exp(-|x - mu| / b)```
+
+Note that the Laplace distribution can be thought of two exponential
+distributions spliced together "back-to-back."
+- - -
+
+#### `tf.contrib.distributions.Laplace.__init__(loc, scale, strict=True, strict_statistics=True, name='Laplace')` {#Laplace.__init__}
+
+Construct Laplace distribution with parameters `loc` and `scale`.
+
+The parameters `loc` and `scale` must be shaped in a way that supports
+broadcasting (e.g., `loc / scale` is a valid operation).
+
+##### Args:
+
+
+*  <b>`loc`</b>: `float` or `double` tensor which characterizes the location (center)
+    of the distribution.
+*  <b>`scale`</b>: `float` or `double`, positive-valued tensor which characterzes the
+    spread of the distribution.
+*  <b>`strict`</b>: Whether to validate input with asserts.  If `strict` is `False`,
+    and the inputs are invalid, correct behavior is not guaranteed.
+*  <b>`strict_statistics`</b>: Boolean, default True.  If True, raise an exception if
+    a statistic (e.g. mean/mode/etc...) is undefined for any batch member.
+    If False, batch members with valid parameters leading to undefined
+    statistics will return NaN for this statistic.
+*  <b>`name`</b>: The name to give Ops created by the initializer.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `loc` and `scale` are of different dtype.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.batch_shape(name='batch_shape')` {#Laplace.batch_shape}
+
+Batch dimensions of this instance as a 1-D int32 `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+  `Tensor` `batch_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.cdf(x, name='cdf')` {#Laplace.cdf}
+
+CDF of observations in `x` under the Laplace distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.dtype` {#Laplace.dtype}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.entropy(name='entropy')` {#Laplace.entropy}
+
+The entropy of Laplace distribution(s).
+
+##### Args:
+
+
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.event_shape(name='event_shape')` {#Laplace.event_shape}
+
+Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+  `Tensor` `event_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.get_batch_shape()` {#Laplace.get_batch_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+  batch shape
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.get_event_shape()` {#Laplace.get_event_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+  event shape
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.is_continuous` {#Laplace.is_continuous}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.is_reparameterized` {#Laplace.is_reparameterized}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.loc` {#Laplace.loc}
+
+Distribution parameter for the location.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.log_cdf(x, name='log_cdf')` {#Laplace.log_cdf}
+
+Log CDF of observations `x` under the Laplace distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.log_pdf(value, name='log_pdf')` {#Laplace.log_pdf}
+
+Log of the probability density function.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.log_pmf(value, name='log_pmf')` {#Laplace.log_pmf}
+
+Log of the probability mass function.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.log_prob(x, name='log_prob')` {#Laplace.log_prob}
+
+Log prob of observations in `x` under these Laplace distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-probability of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.mean(name='mean')` {#Laplace.mean}
+
+Mean of this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.median(name='median')` {#Laplace.median}
+
+Median of this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.mode(name='mode')` {#Laplace.mode}
+
+Mode of this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.name` {#Laplace.name}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.pdf(value, name='pdf')` {#Laplace.pdf}
+
+The probability density function.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.pmf(value, name='pmf')` {#Laplace.pmf}
+
+The probability mass function.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.prob(x, name='pdf')` {#Laplace.prob}
+
+The prob of observations in `x` under the Laplace distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pdf`</b>: tensor of dtype `dtype`, the pdf values of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.sample(n, seed=None, name='sample')` {#Laplace.sample}
+
+Sample `n` observations from the Laplace Distributions.
+
+##### Args:
+
+
+*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`seed`</b>: Python integer, the random seed.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
+    of the distributions determined by broadcasting the parameters.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.scale` {#Laplace.scale}
+
+Distribution parameter for scale.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.std(name='std')` {#Laplace.std}
+
+Standard deviation of this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.strict` {#Laplace.strict}
+
+Boolean describing behavior on invalid input.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.strict_statistics` {#Laplace.strict_statistics}
+
+Boolean describing behavior when a stat is undefined for batch member.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.variance(name='variance')` {#Laplace.variance}
+
+Variance of this distribution.
+
+
+
 - - -
 
 ### `class tf.contrib.distributions.Normal` {#Normal}

tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.split.md

@@ -14,6 +14,17 @@ split0, split1, split2 = tf.split(1, 3, value)
 tf.shape(split0) ==> [5, 10]
 ```
 
+Note: If you are splitting along an axis by the length of that axis, consider
+using unpack, e.g.
+```python
+num_items = t.get_shape()[axis].value
+[tf.squeeze(s, [axis]) for s in tf.split(axis, num_items, t)]
+```
+can be rewritten as
+```python
+tf.unpack(t, axis=axis)
+```
+
 ##### Args:
 
 

tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.pack.md

@@ -2,9 +2,23 @@
 
 Packs a list of rank-`R` tensors into one rank-`(R+1)` tensor.
 
-Packs tensors in `values` into a tensor with rank one higher than each tensor
-in `values` and shape `[len(values)] + values[0].shape`. The output satisfies
-`output[i, ...] = values[i][...]`.
+Packs the list of tensors in `values` into a tensor with rank one higher than
+each tensor in `values`, by packing them along the `axis` dimension.
+Given a list of length `N` of tensors of shape `(A, B, C)`;
+
+if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.
+if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.
+Etc.
+
+For example:
+
+```prettyprint
+# 'x' is [1, 4]
+# 'y' is [2, 5]
+# 'z' is [3, 6]
+pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]]  # Pack along first dim.
+pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
+```
 
 This is the opposite of unpack.  The numpy equivalent is
 

tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.unpack.md

@@ -2,12 +2,19 @@
 
 Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
 
-Unpacks `num` tensors from `value` along the given dimension.
+Unpacks `num` tensors from `value` by chipping it along the `axis` dimension.
 If `num` is not specified (the default), it is inferred from `value`'s shape.
 If `value.shape[axis]` is not known, `ValueError` is raised.
 
-The ith tensor in `output` is the slice `value[i, ...]`. Each tensor in
-`output` has shape `value.shape[1:]`.
+For example, given a tensor of shape `(A, B, C, D)`;
+
+If `axis == 0` then the i'th tensor in `output` is the slice
+  `value[i, :, :, :]` and each tensor in `output` will have shape `(B, C, D)`.
+  (Note that the dimension unpacked along is gone, unlike `split`).
+
+If `axis == 1` then the i'th tensor in `output` is the slice
+  `value[:, i, :, :]` and each tensor in `output` will have shape `(A, C, D)`.
+Etc.
 
 This is the opposite of pack.  The numpy equivalent is
 

tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.concat.md

@@ -32,6 +32,16 @@ tf.shape(tf.concat(0, [t3, t4])) ==> [4, 3]
 tf.shape(tf.concat(1, [t3, t4])) ==> [2, 6]
 ```
 
+Note: If you are concatenating along a new axis consider using pack.
+E.g.
+```python
+tf.concat(axis, [tf.expand_dims(t, axis) for t in ts])
+```
+can be rewritten as
+```
+tf.pack(tensors, axis=axis)
+```
+
 ##### Args:
 
 

tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md

+The Laplace distribution with location and scale > 0 parameters.
+
+#### Mathematical details
+
+The PDF of this distribution is:
+
+```f(x | mu, b, b > 0) = 0.5 / b exp(-|x - mu| / b)```
+
+Note that the Laplace distribution can be thought of two exponential
+distributions spliced together "back-to-back."
+- - -
+
+#### `tf.contrib.distributions.Laplace.__init__(loc, scale, strict=True, strict_statistics=True, name='Laplace')` {#Laplace.__init__}
+
+Construct Laplace distribution with parameters `loc` and `scale`.
+
+The parameters `loc` and `scale` must be shaped in a way that supports
+broadcasting (e.g., `loc / scale` is a valid operation).
+
+##### Args:
+
+
+*  <b>`loc`</b>: `float` or `double` tensor which characterizes the location (center)
+    of the distribution.
+*  <b>`scale`</b>: `float` or `double`, positive-valued tensor which characterzes the
+    spread of the distribution.
+*  <b>`strict`</b>: Whether to validate input with asserts.  If `strict` is `False`,
+    and the inputs are invalid, correct behavior is not guaranteed.
+*  <b>`strict_statistics`</b>: Boolean, default True.  If True, raise an exception if
+    a statistic (e.g. mean/mode/etc...) is undefined for any batch member.
+    If False, batch members with valid parameters leading to undefined
+    statistics will return NaN for this statistic.
+*  <b>`name`</b>: The name to give Ops created by the initializer.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `loc` and `scale` are of different dtype.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.batch_shape(name='batch_shape')` {#Laplace.batch_shape}
+
+Batch dimensions of this instance as a 1-D int32 `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+  `Tensor` `batch_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.cdf(x, name='cdf')` {#Laplace.cdf}
+
+CDF of observations in `x` under the Laplace distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.dtype` {#Laplace.dtype}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.entropy(name='entropy')` {#Laplace.entropy}
+
+The entropy of Laplace distribution(s).
+
+##### Args:
+
+
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.event_shape(name='event_shape')` {#Laplace.event_shape}
+
+Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+  `Tensor` `event_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.get_batch_shape()` {#Laplace.get_batch_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+  batch shape
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.get_event_shape()` {#Laplace.get_event_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+  event shape
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.is_continuous` {#Laplace.is_continuous}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.is_reparameterized` {#Laplace.is_reparameterized}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.loc` {#Laplace.loc}
+
+Distribution parameter for the location.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.log_cdf(x, name='log_cdf')` {#Laplace.log_cdf}
+
+Log CDF of observations `x` under the Laplace distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.log_pdf(value, name='log_pdf')` {#Laplace.log_pdf}
+
+Log of the probability density function.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.log_pmf(value, name='log_pmf')` {#Laplace.log_pmf}
+
+Log of the probability mass function.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.log_prob(x, name='log_prob')` {#Laplace.log_prob}
+
+Log prob of observations in `x` under these Laplace distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-probability of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.mean(name='mean')` {#Laplace.mean}
+
+Mean of this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.median(name='median')` {#Laplace.median}
+
+Median of this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.mode(name='mode')` {#Laplace.mode}
+
+Mode of this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.name` {#Laplace.name}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.pdf(value, name='pdf')` {#Laplace.pdf}
+
+The probability density function.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.pmf(value, name='pmf')` {#Laplace.pmf}
+
+The probability mass function.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.prob(x, name='pdf')` {#Laplace.prob}
+
+The prob of observations in `x` under the Laplace distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pdf`</b>: tensor of dtype `dtype`, the pdf values of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.sample(n, seed=None, name='sample')` {#Laplace.sample}
+
+Sample `n` observations from the Laplace Distributions.
+
+##### Args:
+
+
+*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`seed`</b>: Python integer, the random seed.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
+    of the distributions determined by broadcasting the parameters.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.scale` {#Laplace.scale}
+
+Distribution parameter for scale.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.std(name='std')` {#Laplace.std}
+
+Standard deviation of this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.strict` {#Laplace.strict}
+
+Boolean describing behavior on invalid input.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.strict_statistics` {#Laplace.strict_statistics}
+
+Boolean describing behavior when a stat is undefined for batch member.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.variance(name='variance')` {#Laplace.variance}
+
+Variance of this distribution.
+
+

tensorflow/g3doc/api_docs/python/index.md

@@ -590,6 +590,7 @@
   * [`Exponential`](../../api_docs/python/contrib.distributions.md#Exponential)
   * [`Gamma`](../../api_docs/python/contrib.distributions.md#Gamma)
   * [`kl`](../../api_docs/python/contrib.distributions.md#kl)
+  * [`Laplace`](../../api_docs/python/contrib.distributions.md#Laplace)
   * [`MultivariateNormalCholesky`](../../api_docs/python/contrib.distributions.md#MultivariateNormalCholesky)
   * [`MultivariateNormalFull`](../../api_docs/python/contrib.distributions.md#MultivariateNormalFull)
   * [`Normal`](../../api_docs/python/contrib.distributions.md#Normal)