Python Docstring Convetion (#16550)

* Docstring convetnion for

* Docstring convention for

* Docstring convention for

* Docstring convention for

* Docstring convention for

* Docstring convention for

* Docstring convention

* Revert removing new line

* Remove white space

python/mxnet/kvstore.py

@@ -31,8 +31,7 @@
 from .profiler import set_kvstore_handle
 
 def _ctype_key_value(keys, vals):
-    """
-    Returns ctype arrays for the key-value args, and the whether string keys are used.
+    """Returns ctype arrays for the key-value args, and the whether string keys are used.
     For internal use only.
     """
     if isinstance(keys, (tuple, list)):
@@ -66,9 +65,7 @@ def _ctype_key_value(keys, vals):
         return (c_keys, c_handle_array(vals), use_str_keys)
 
 def _ctype_dict(param_dict):
-    """
-    Returns ctype arrays for keys and values(converted to strings) in a dictionary
-    """
+    """Returns ctype arrays for keys and values(converted to strings) in a dictionary"""
     assert(isinstance(param_dict, dict)), \
         "unexpected type for param_dict: " + str(type(param_dict))
     c_keys = c_array(ctypes.c_char_p, [c_str(k) for k in param_dict.keys()])

python/mxnet/metric.py

@@ -153,8 +153,7 @@ def reset(self):
         self.global_sum_metric = 0.0
 
     def reset_local(self):
-        """Resets the local portion of the internal evaluation results
-        to initial state."""
+        """Resets the local portion of the internal evaluation results to initial state."""
         self.num_inst = 0
         self.sum_metric = 0.0
 
@@ -372,8 +371,7 @@ def reset(self):
             pass
 
     def reset_local(self):
-        """Resets the local portion of the internal evaluation results
-        to initial state."""
+        """Resets the local portion of the internal evaluation results to initial state."""
         try:
             for metric in self.metrics:
                 metric.reset_local()
@@ -592,8 +590,7 @@ def update(self, labels, preds):
 
 
 class _BinaryClassificationMetrics(object):
-    """
-    Private container class for classification metric statistics. True/false positive and
+    """Private container class for classification metric statistics. True/false positive and
      true/false negative counts are sufficient statistics for various classification metrics.
     This class provides the machinery to track those statistics across mini-batches of
     (label, prediction) pairs.
@@ -610,9 +607,7 @@ def __init__(self):
         self.global_true_negatives = 0
 
     def update_binary_stats(self, label, pred):
-        """
-        Update various binary classification counts for a single (label, pred)
-        pair.
+        """Update various binary classification counts for a single (label, pred) pair.
 
         Parameters
         ----------
@@ -691,9 +686,7 @@ def global_fscore(self):
             return 0.
 
     def matthewscc(self, use_global=False):
-        """
-        Calculate the Matthew's Correlation Coefficent
-        """
+        """Calculate the Matthew's Correlation Coefficent"""
         if use_global:
             if not self.global_total_examples:
                 return 0.
@@ -1604,8 +1597,7 @@ def reset(self):
         self.reset_local()
 
     def reset_local(self):
-        """Resets the local portion of the internal evaluation results
-        to initial state."""
+        """Resets the local portion of the internal evaluation results to initial state."""
         self.num_inst = 0.
         self.lcm = numpy.zeros((self.k, self.k))
 

python/mxnet/profiler.py

@@ -207,8 +207,7 @@ def pause(profile_process='worker'):
 
 
 def resume(profile_process='worker'):
-    """
-    Resume paused profiling.
+    """Resume paused profiling.
 
     Parameters
     ----------

python/mxnet/rtc.py

@@ -172,7 +172,8 @@ def get_kernel(self, name, signature):
 
 class CudaKernel(object):
     """Constructs CUDA kernel. Should be created by `CudaModule.get_kernel`,
-    not intended to be used by users."""
+    not intended to be used by users.
+    """
     def __init__(self, handle, name, is_ndarray, dtypes):
         self.handle = handle
         self._name = name

python/mxnet/runtime.py

@@ -26,26 +26,20 @@
 from .base import _LIB, check_call
 
 class Feature(ctypes.Structure):
-    """
-    Compile time feature description, member fields: `name` and `enabled`.
-    """
+    """Compile time feature description, member fields: `name` and `enabled`."""
     _fields_ = [
         ("_name", ctypes.c_char_p),
         ("_enabled", ctypes.c_bool)
     ]
 
     @property
     def name(self):
-        """
-        Feature name.
-        """
+        """Feature name."""
         return self._name.decode()
 
     @property
     def enabled(self):
-        """
-        True if MXNet was compiled with the given compile-time feature.
-        """
+        """True if MXNet was compiled with the given compile-time feature."""
         return self._enabled
 
     def __repr__(self):
@@ -55,8 +49,7 @@ def __repr__(self):
             return "✖ {}".format(self.name)
 
 def feature_list():
-    """
-    Check the library for compile-time features. The list of features are maintained in libinfo.h and libinfo.cc
+    """Check the library for compile-time features. The list of features are maintained in libinfo.h and libinfo.cc
 
     Returns
     -------
@@ -70,9 +63,7 @@ def feature_list():
     return features
 
 class Features(collections.OrderedDict):
-    """
-    OrderedDict of name to Feature
-    """
+    """OrderedDict of name to Feature"""
     instance = None
     def __new__(cls):
         if cls.instance is None:
@@ -84,8 +75,7 @@ def __repr__(self):
         return str(list(self.values()))
 
     def is_enabled(self, feature_name):
-        """
-        Check for a particular feature by name
+        """Check for a particular feature by name
 
         Parameters
         ----------

python/mxnet/test_utils.py

@@ -1935,8 +1935,7 @@ def same_array(array1, array2):
 
 @contextmanager
 def discard_stderr():
-    """
-    Discards error output of a routine if invoked as:
+    """Discards error output of a routine if invoked as:
 
     with discard_stderr():
         ...
@@ -2324,7 +2323,8 @@ def __exit__(self, ptype, value, trace):
 
 def collapse_sum_like(a, shape):
     """Given `a` as a numpy ndarray, perform reduce_sum on `a` over the axes that do not
-    exist in `shape`. Note that an ndarray with `shape` must be broadcastable to `a`."""
+    exist in `shape`. Note that an ndarray with `shape` must be broadcastable to `a`.
+    """
     assert len(a.shape) >= len(shape)
     if np.prod(shape) == 0 or a.size == 0:
         return np.zeros(shape, dtype=a.dtype)
@@ -2349,7 +2349,8 @@ def is_cd_run():
 
 def has_tvm_ops():
     """Returns True if MXNet is compiled with TVM generated operators. If current ctx
-    is GPU, it only returns True for CUDA compute capability > 52 where FP16 is supported."""
+    is GPU, it only returns True for CUDA compute capability > 52 where FP16 is supported.
+    """
     built_with_tvm_op = _features.is_enabled("TVM_OP")
     ctx = current_context()
     if ctx.device_type == 'gpu':
@@ -2367,7 +2368,8 @@ def has_tvm_ops():
 def is_op_runnable():
     """Returns True for all CPU tests. Returns True for GPU tests that are either of the following.
     1. Built with USE_TVM_OP=0.
-    2. Built with USE_TVM_OP=1, but with compute capability >= 53."""
+    2. Built with USE_TVM_OP=1, but with compute capability >= 53.
+    """
     ctx = current_context()
     if ctx.device_type == 'gpu':
         if not _features.is_enabled("TVM_OP"):

python/mxnet/util.py

@@ -60,8 +60,7 @@ def get_gpu_memory(gpu_dev_id):
 
 
 def set_np_shape(active):
-    """
-    Turns on/off NumPy shape semantics, in which `()` represents the shape of scalar tensors,
+    """Turns on/off NumPy shape semantics, in which `()` represents the shape of scalar tensors,
     and tuples with `0` elements, for example, `(0,)`, `(1, 0, 2)`, represent the shapes
     of zero-size tensors. This is turned off by default for keeping backward compatibility.
 
@@ -568,8 +567,7 @@ def hybrid_forward(self, F, x, w):
 
 
 def np_ufunc_legal_option(key, value):
-    """
-    Checking if ufunc arguments are legal inputs
+    """Checking if ufunc arguments are legal inputs
 
     Parameters
     ----------