Update generated Python Op docs.

Change: 138697361

1 files changed, 56 insertions, 11 deletions

diff --git a/tensorflow/g3doc/api_docs/python/math_ops.md b/tensorflow/g3doc/api_docs/python/math_ops.md
index 0cbafd7d2b..95062d5ffe 100644
--- a/tensorflow/g3doc/api_docs/python/math_ops.md
+++ b/tensorflow/g3doc/api_docs/python/math_ops.md
@@ -2571,32 +2571,77 @@ tf.accumulate_n([a, b, a], shape=[2, 2], tensor_dtype=tf.int32)
 
 - - -
 
-### `tf.einsum(axes, *inputs)` {#einsum}
+### `tf.einsum(equation, *inputs)` {#einsum}
 
 A generalized contraction between tensors of arbitrary dimension.
 
-Like `numpy.einsum`, but does not support:
+This function returns a tensor whose elements are defined by `equation`,
+which is written in a shorthand form inspired by the Einstein summation
+convention.  As an example, consider multiplying two matrices
+A and B to form a matrix C.  The elements of C are given by:
+
+```
+  C[i,k] = sum_j A[i,j] * B[j,k]
+```
+
+The corresponding `equation` is:
+
+```
+  ij,jk->ik
+```
+
+In general, the `equation` is obtained from the more familiar element-wise
+equation by
+  1. removing variable names, brackets, and commas,
+  2. replacing "*" with ",",
+  3. dropping summation signs, and
+  4. moving the output to the right, and replacing "=" with "->".
+
+Many common operations can be expressed in this way.  For example:
+
+# Matrix multiplication
+>>> einsum('ij,jk->ik', m0, m1)  # output[i,k] = sum_j m0[i,j] * m1[j, k]
+
+# Dot product
+>>> einsum('i,i->', u, v)  # output = sum_i u[i]*v[i]
+
+# Outer product
+>>> einsum('i,j->ij', u, v)  # output[i,j] = u[i]*v[j]
+
+# Transpose
+>>> einsum('ij->ji', m)  # output[j,i] = m[i,j]
+
+# Batch matrix multiplication
+>>> einsum('aij,ajk->aik', s, t)  # out[a,i,k] = sum_j s[a,i,j] * t[a, j, k]
+
+This function behaves like `numpy.einsum`, but does not support:
 * Ellipses (subscripts like `ij...,jk...->ik...`)
-* Subscripts where an axis appears more than once for a single input (e.g. `ijj,jk->ik`).
+* Subscripts where an axis appears more than once for a single input
+  (e.g. `ijj,k->ik`).
+* Subscripts that are summed across multiple inputs (e.g., `ij,ij,jk->ik`).
 
 ##### Args:
 
 
-*  <b>`axes`</b>: a `str` describing the contraction, in the same format as `numpy.einsum`.
-*  <b>`inputs`</b>: the inputs to contract (each one a `Tensor`), whose shapes should be consistent with `axes`.
+*  <b>`equation`</b>: a `str` describing the contraction, in the same format as
+    `numpy.einsum`.
+*  <b>`inputs`</b>: the inputs to contract (each one a `Tensor`), whose shapes should
+    be consistent with `equation`.
 
 ##### Returns:
 
-  The contracted `Tensor`, with shape determined by `axes`.
+  The contracted `Tensor`, with shape determined by `equation`.
 
 ##### Raises:
 
 
-*  <b>`ValueError`</b>: If the format of `axes` is incorrect,
-              or the number of inputs implied by `axes` does not match `len(inputs)`,
-              or an axis appears in the output subscripts but not in any of the inputs,
-              or the number of dimensions of an input differs from the number of indices in its subscript,
-              or the input shapes are inconsistent along a particular axis.
+*  <b>`ValueError`</b>: If
+    - the format of `equation` is incorrect,
+    - the number of inputs implied by `equation` does not match `len(inputs)`,
+    - an axis appears in the output subscripts but not in any of the inputs,
+    - the number of dimensions of an input differs from the number of
+      indices in its subscript, or
+    - the input shapes are inconsistent along a particular axis.