Fix lint

python/mxnet/ndarray/numpy/_op.py

@@ -4907,5 +4907,5 @@ def nonzero(a):
     >>> (a > 3).nonzero()
     (array([1, 1, 1, 2, 2, 2], dtype=int64), array([0, 1, 2, 0, 1, 2], dtype=int64))
     """
-    indices = _npi.nonzero(a).transpose()
-    return tuple([indices[i] for i in range(len(indices))])
+    out = _npi.nonzero(a).transpose()
+    return tuple([out[i] for i in range(len(out))])

python/mxnet/ndarray/numpy/random.py

@@ -27,7 +27,7 @@
 
 
 def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
-    """Return random integers from `low` (inclusive) to `high` (exclusive).
+    r"""Return random integers from `low` (inclusive) to `high` (exclusive).
 
     Return random integers from the "discrete uniform" distribution of
     the specified dtype in the "half-open" interval [`low`, `high`). If
@@ -88,7 +88,7 @@ def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
 
 
 def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
-    """Draw samples from a uniform distribution.
+    r"""Draw samples from a uniform distribution.
 
     Samples are uniformly distributed over the half-open interval
     ``[low, high)`` (includes low, but excludes high).  In other words,
@@ -143,7 +143,7 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
 
 
 def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
-    """Draw random samples from a normal (Gaussian) distribution.
+    r"""Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
     by *loc* (mean) and *scale* (standard deviation).
@@ -194,7 +194,7 @@ def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
 
 
 def multinomial(n, pvals, size=None):
-    """multinomial(n, pvals, size=None)
+    r"""multinomial(n, pvals, size=None)
 
     Draw samples from a multinomial distribution.
 
@@ -246,7 +246,7 @@ def multinomial(n, pvals, size=None):
 
 
 def choice(a, size=None, replace=True, p=None, ctx=None, out=None):
-    """Generates a random sample from a given 1-D array
+    r"""Generates a random sample from a given 1-D array
 
     Parameters
     -----------

python/mxnet/numpy/multiarray.py

@@ -6900,5 +6900,5 @@ def nonzero(a):
     >>> (a > 3).nonzero()
     (array([1, 1, 1, 2, 2, 2], dtype=int64), array([0, 1, 2, 0, 1, 2], dtype=int64))
     """
-    indices = _npi.nonzero(a).transpose()
-    return tuple([indices[i] for i in range(len(indices))])
+    out = _npi.nonzero(a).transpose()
+    return tuple([out[i] for i in range(len(out))])

python/mxnet/numpy/random.py

@@ -24,7 +24,7 @@
 
 
 def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
-    """Return random integers from `low` (inclusive) to `high` (exclusive).
+    r"""Return random integers from `low` (inclusive) to `high` (exclusive).
 
     Return random integers from the "discrete uniform" distribution of
     the specified dtype in the "half-open" interval [`low`, `high`). If
@@ -76,7 +76,7 @@ def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
 
 
 def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
-    """Draw samples from a uniform distribution.
+    r"""Draw samples from a uniform distribution.
 
     Samples are uniformly distributed over the half-open interval
     ``[low, high)`` (includes low, but excludes high).  In other words,
@@ -132,7 +132,7 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
 
 
 def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
-    """Draw random samples from a normal (Gaussian) distribution.
+    r"""Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
     by *loc* (mean) and *scale* (standard deviation).
@@ -200,7 +200,7 @@ def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
 
 
 def multinomial(n, pvals, size=None, **kwargs):
-    """
+    r"""
     Draw samples from a multinomial distribution.
     The multinomial distribution is a multivariate generalisation of the binomial distribution.
     Take an experiment with one of ``p`` possible outcomes. An example of such an experiment is throwing a dice,
@@ -242,7 +242,7 @@ def multinomial(n, pvals, size=None, **kwargs):
 
 
 def choice(a, size=None, replace=True, p=None, ctx=None, out=None):
-    """Generates a random sample from a given 1-D array
+    r"""Generates a random sample from a given 1-D array
 
     Parameters
     -----------

python/mxnet/numpy_extension/random.py

@@ -25,7 +25,7 @@
 
 
 def seed(seed, ctx='all'):  # pylint: disable=redefined-outer-name
-    """Seeds the random number generators in MXNet.
+    r"""Seeds the random number generators in MXNet.
 
     This affects the behavior of modules in MXNet that uses random number generators,
     like the dropout operator and `ndarray`'s random sampling operators.

python/mxnet/symbol/numpy/random.py

@@ -25,7 +25,7 @@
 
 
 def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
-    """Return random integers from `low` (inclusive) to `high` (exclusive).
+    r"""Return random integers from `low` (inclusive) to `high` (exclusive).
 
     Return random integers from the "discrete uniform" distribution of
     the specified dtype in the "half-open" interval [`low`, `high`). If
@@ -113,7 +113,7 @@ def rand(*size, **kwargs):
 
 
 def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
-    """Draw samples from a uniform distribution.
+    r"""Draw samples from a uniform distribution.
 
     Samples are uniformly distributed over the half-open interval
     ``[low, high)`` (includes low, but excludes high).  In other words,
@@ -168,7 +168,7 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
 
 
 def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
-    """Draw random samples from a normal (Gaussian) distribution.
+    r"""Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
     by *loc* (mean) and *scale* (standard deviation).
@@ -217,7 +217,7 @@ def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
 
 
 def choice(a, size=None, replace=True, p=None, ctx=None, out=None):
-    """Generates a random sample from a given 1-D array
+    r"""Generates a random sample from a given 1-D array
 
     Parameters
     -----------