format fixes (#20030)

docs/ops/generation/Multinomial_13.md

@@ -15,31 +15,35 @@
 
 **Algorithm formulation**:
 
-Note: The following notation
+.. note::
+
+  The following notation denotes a range of real numbers between a and b.
 
 .. math::
 
    [a, b] => { x \in \mathbb{R},  a <= x <= b }
 
-denotes a range of real numbers between a and b.
 
 Given a list of probabilities x1, x2, ..., xn:
 
 * If *log_probs* is true:
-  * For each probability x, replace it with a value e^x
+
-* Create an array - discrete CDF ([Cumulative Distribution Function](https://en.wikipedia.org/wiki/Cumulative_distribution_function)) - the cumulative sum of those probabilities, ie. create an array of values where the ith value is the sum of the probabilities x1, ..., xi
+  * For each probability x, replace it with a value :math:`e^{x}`.
+
+* Create an array - discrete CDF (`Cumulative Distribution Function <https://en.wikipedia.org/wiki/Cumulative_distribution_function>`__) - the cumulative sum of those probabilities, ie. create an array of values where the ith value is the sum of the probabilities x1, ..., xi.
 * Divide the created array by its maximum value to normalize the cumulative probabilities between the real values in the range [0, 1]. This array is, by definition of CDF, sorted in ascending order, hence the maximum value is the last value of the array.
 * Randomly generate a sequence of double-precision floating point numbers in the range [0, 1].
 * For each generated number, assign the class with the lowest index for which the cumulative probability is less or equal to the generated value.
 * If *with_replacement* is False (sampling without replacement):
 
   * Assume a class with index i has been selected - then every CDF value starting at i-th index should be lowered by the original probability of the selected class. This effectively sets the probability of sampling the given class to 0.
-  * Afterwards, divide the CDF by its last (maximum) value to normalize the cumulative probabilities between the real values in the range [0, 1]
+  * Afterwards, divide the CDF by its last (maximum) value to normalize the cumulative probabilities between the real values in the range [0, 1].
 
-* Convert the output indices to *convert_type*
+* Convert the output indices to *convert_type*.
-* Return output indices
+* Return output indices.
 
 **Example computations**:
+
 Example 1 - 1D tensor
 
 * Let ``probs`` = ``[0.1, 0.5, 0.4]``, ``num_samples`` = 5, ``log_probs`` = false, ``with_replacement`` = true
@@ -67,7 +71,7 @@ Example 3 - 1D tensor, without replacement
   * Therefore, in CDF, for every class starting with idx ``1`` subtract the probability of class at idx ``1`` = ``probs[1]`` = 0.5
   * CDF = ``[0.1, 0.6 - 0.5, 1.0 - 0.5]`` = ``[0.1, 0.1, 0.5]``
   * Normalize CDF by dividing by last value: CDF = ``[0.2, 0.2, 1.0]``
-  * Take the next randomly generated float, here 0.2, and repeat until all random samples have assigned classes. Notice that for ``sampled values`` <= 0.2, only the class wih idx ``0`` can be selected, since the search stops at the index with the first value satisfying ``sample value`` <= ``CDF probability``
+  * Take the next randomly generated float, here 0.2, and repeat until all random samples have assigned classes. Notice that for ``sampled values`` <= 0.2, only the class with idx ``0`` can be selected, since the search stops at the index with the first value satisfying ``sample value`` <= ``CDF probability``
 
 * Assigned classes = ``[1, 2]``
 
@@ -86,8 +90,8 @@ Example 3 - 1D tensor, without replacement
   * **Description**: controls whether to sample with replacement (classes can be sampled multiple times).
   * **Range of values**: `true`, `false`
   
-      * ``true`` - class indices can be sampled multiple times.
+    * ``true`` - class indices can be sampled multiple times.
-      * ``false`` - class indices will not repeat in the output and the size of ``probs``' ``class_size`` dimension is required to be larger or equal to *num_samples* value. Might affect performance.
+    * ``false`` - class indices will not repeat in the output and the size of ``probs``' ``class_size`` dimension is required to be larger or equal to *num_samples* value. Might affect performance.
   
   * **Type**: `bool`
   * **Required**: *Yes*
@@ -97,8 +101,8 @@ Example 3 - 1D tensor, without replacement
   * **Description**: allows to control whether *inputs* should be treated as probabilities or unnormalized log probabilities.
   * **Range of values**: `true`, `false`
 
-      * ``true`` - set values in *inputs* are unnormalized log probabilities that can be any real number.
+    * ``true`` - set values in *inputs* are unnormalized log probabilities that can be any real number.
-      * ``false`` - probabilities in *inputs* are expected to be non-negative, finite and have a non-zero-sum.
+    * ``false`` - probabilities in *inputs* are expected to be non-negative, finite and have a non-zero-sum.
 
   * **Type**: `bool`
   * **Required**: *Yes*