take2: Docstring only changes in quantization, fake_quantize, and observer (#27574)

* docstring only formatting changes in the quantize.py and fake_quantization.py files to render better in HTML.

* docstring change on observer.py as well

* just kind of tweaking the docstrings a bit more.

* switching to r""" for the mult-line string. Per Zafar's suggestion.

* trying to resolve the merge conflict soumith saw

* trying to avoid a conflict when this gets merged back to master

torch/quantization/fake_quantize.py

@@ -4,13 +4,11 @@ from torch.nn import Module
 from .observer import MovingAverageMinMaxObserver, HistogramObserver, MovingAveragePerChannelMinMaxObserver, _with_args
 
 class FakeQuantize(Module):
-    ''' Simulate the quantize and dequantize operations in training time.
+    r""" Simulate the quantize and dequantize operations in training time.
     The output of this module is given by
 
     x_out = (clamp(round(x/scale + zero_point), quant_min, quant_max)-zero_point)*scale
 
-
-
     * :attr:`scale` defines the scale factor used for quantization.
 
     * :attr:`zero_point` specifies the quantized value to which 0 in floating point maps to
@@ -25,13 +23,13 @@ class FakeQuantize(Module):
     * :attr:`observer_enable` controls statistics collection on tensors
 
     * :attr:`dtype` specifies the quantized dtype that is being emulated with fake-quantization,
-     allowable values are torch.qint8 and torch.quint8. The values of quant_min and quant_max should
-     be chosen to be consistent with the dtype
+                    allowable values are torch.qint8 and torch.quint8. The values of quant_min and 
+                    quant_max should be chosen to be consistent with the dtype
 
 
     Args:
-        observer (module): Module for observing statistics on input tensors and calculating
-        scale and zero-point.
+        observer (module): Module for observing statistics on input tensors and calculating scale 
+                           and zero-point.
         quant_min (int): The minimum allowable quantized value.
         quant_max (int): The maximum allowable quantized value.
         observer_kwargs (optional): Arguments for the observer module
@@ -39,15 +37,7 @@ class FakeQuantize(Module):
     Attributes:
         observer (Module): User provided module that collects statistics on the input tensor and
                            provides a method to calculate scale and zero-point.
-
     """
-    Args:
-        `observer`: Observer module that records stats of input tensor
-        `quant_min`: Tensors are fake-quantized corresponding to the
-        `quant_max`: A function that calculates quantization parameters
-        given the stats
-        `observer_kwargs`
-    '''
     def __init__(self, observer=MovingAverageMinMaxObserver, quant_min=0, quant_max=255, **observer_kwargs):
         super(FakeQuantize, self).__init__()
         assert quant_min <= quant_max, \

torch/quantization/quantize.py

@@ -80,12 +80,10 @@ def add_observer_(module):
     has a valid qconfig attribute.
 
     Args:
-        module: input module with qconfig attributes for all the leaf modules
-        that we want to quantize
+        module: input module with qconfig attributes for all the leaf modules that we want to quantize
 
     Return:
-        None, module is modified inplace with added observer modules and
-            forward_hooks
+        None, module is modified inplace with added observer modules and forward_hooks
     """
     for child in module.children():
         if type(child) == nnq.FloatFunctional:
@@ -252,7 +250,8 @@ def quantize_qat(model, run_fn, run_args, inplace=False):
     Args:
         model: input model
         run_fn: a function for evaluating the prepared model, can be a
-            function that simply runs the prepared model or a training loop
+                function that simply runs the prepared model or a training 
+                loop
         run_args: positional arguments for `run_fn`
 
     Return:
@@ -269,10 +268,11 @@ def quantize_qat(model, run_fn, run_args, inplace=False):
 def convert(module, mapping=None, inplace=False):
     r"""Converts the float module with observers (where we can get quantization
     parameters) to a quantized module.
+
     Args:
         module: calibrated module with observers
-        mapping: a dictionary that maps from float module type to quantized
-           module type, can be overwrritten to allow swapping user defined Modules
+        mapping: a dictionary that maps from float module type to quantized module type, can 
+                 be overwrritten to allow swapping user defined Modules
         inplace: carry out model transformations in-place, the original module is mutated
     """
     if mapping is None: