fix codestyle

python/paddle/tensor/linalg.py

@@ -3734,13 +3734,15 @@ def cdist(
     )
 
 
-def histogramdd(sample, bins=10, range=None, density=False, weights=None, name=None):
+def histogramdd(
+    sample, bins=10, range=None, density=False, weights=None, name=None
+):
     r"""
     Computes a multi-dimensional histogram of the values in a tensor.
     Interprets the elements of an input tensor whose innermost dimension has size N as a collection of N-dimensional points. Maps each of the points into a set of N-dimensional bins and returns the number of points (or total weight) in each bin.
     input must be a tensor with at least 2 dimensions. If input has shape (M, N), each of its M rows defines a point in N-dimensional space. If input has three or more dimensions, all but the last dimension are flattened.
-    Each dimension is independently associated with its own strictly increasing sequence of bin edges. Bin edges may be specified explicitly by passing a sequence of 1D tensors. Alternatively, bin edges may be constructed automatically by passing a sequence of integers specifying the number of equal-width bins in each dimension.    
-    
+    Each dimension is independently associated with its own strictly increasing sequence of bin edges. Bin edges may be specified explicitly by passing a sequence of 1D tensors. Alternatively, bin edges may be constructed automatically by passing a sequence of integers specifying the number of equal-width bins in each dimension.
+
     Args:
         sample (Tensor): The input tensor.
         bins (Tensor[], int[], or int): If Tensor[], defines the sequences of bin edges. If int[], defines the number of equal-width bins in each dimension. If int, defines the number of equal-width bins for all dimensions.
@@ -3782,10 +3784,11 @@ def histogramdd(sample, bins=10, range=None, density=False, weights=None, name=N
 
 
     """
+
     def __check_sample(sample):
-        assert len(sample.shape) >= 2, (
-            "input sample must be a tensor with at least 2 dimensions."
-        )
+        assert (
+            len(sample.shape) >= 2
+        ), "input sample must be a tensor with at least 2 dimensions."
         check_variable_and_dtype(
             sample,
             'sample',
@@ -3795,6 +3798,7 @@ def __check_sample(sample):
             ],
             'histogramdd',
         )
+
     def __check_bins(bins, sample):  # when Tensor[], check dtype
         for bins_tensor in bins:
             bins_tensor = paddle.to_tensor(bins_tensor)
@@ -3807,12 +3811,13 @@ def __check_bins(bins, sample):  # when Tensor[], check dtype
                 ],
                 'histogramdd',
             )
-            assert bins_tensor.dtype == sample.dtype, (
-                "When bins is Tensor[], the dtype of bins must be the same as sample.\n"
-            )
-        
+            assert (
+                bins_tensor.dtype == sample.dtype
+            ), "When bins is Tensor[], the dtype of bins must be the same as sample.\n"
+
     def __check_weights(sample, weights):
-        if weights is None: return
+        if weights is None:
+            return
         sample_shape, weights_shape = sample.shape, weights.shape
         assert len(sample_shape) == len(weights_shape) + 1, (
             "if weight tensor is provided,"
@@ -3832,17 +3837,20 @@ def __check_weights(sample, weights):
             ],
             'histogramdd',
         )
-        assert weights.dtype == sample.dtype, (
-            "The dtype of weights must be the same as sample.\n"
-        )
+        assert (
+            weights.dtype == sample.dtype
+        ), "The dtype of weights must be the same as sample.\n"
+
     def __check_range(D, range):
-        if range is None: return
+        if range is None:
+            return
         check_type(range, 'range', (list, tuple), 'histogramdd')
-        assert D * 2 == len(range), (
-            "The length of range list must be %d\n" % (D * 2)
+        assert D * 2 == len(range), "The length of range list must be %d\n" % (
+            D * 2
         )
+
     check_type(density, 'density', bool, 'histogramdd')
-    
+
     __check_sample(sample)
     # weights
     __check_weights(sample, weights)
@@ -3867,19 +3875,17 @@ def __check_range(D, range):
             range[:, 0] = minv
             range[:, 1] = maxv
         else:
-            range = paddle.static.setitem(
-                range, (slice(None), 0), minv
-            )
-            range = paddle.static.setitem(
-                range, (slice(None), 1), maxv
-            )
-    else: range = paddle.to_tensor(range, dtype=paddle.float32).reshape([D, 2])
+            range = paddle.static.setitem(range, (slice(None), 0), minv)
+            range = paddle.static.setitem(range, (slice(None), 1), maxv)
+    else:
+        range = paddle.to_tensor(range, dtype=paddle.float32).reshape([D, 2])
     # bins to edges
     edges = []
     hist_shape = []
     dedges = []
-    if isinstance(bins, (int, list)): # int or int[]
-        if isinstance(bins, int): bins = [bins] * D
+    if isinstance(bins, (int, list)):  # int or int[]
+        if isinstance(bins, int):
+            bins = [bins] * D
         assert len(bins) == D, (
             "The length of bins must be %d when bins is a list.\n" % D
         )
@@ -3891,26 +3897,28 @@ def __check_range(D, range):
             e = paddle.linspace(r[0], r[1], bins[idx] + 1, 'float32')
             edges.append(e)
             dedges.append(e.diff())
-    elif isinstance(bins, tuple):  # tuple with D tensors for each innermost dimension
+    elif isinstance(
+        bins, tuple
+    ):  # tuple with D tensors for each innermost dimension
         __check_bins(bins, sample)
         for bin in bins:
             bin = paddle.to_tensor(bin)
             edges.append(bin)
             dedges.append(bin.diff())
     else:
-        raise ValueError(
-            "Input bins must be Tensor[], int[], or int."
-        )
+        raise ValueError("Input bins must be Tensor[], int[], or int.")
     hist_shape = [edge.shape[0] + 1 for edge in edges]
     index_list = []
     # edges shape: [D, linspaced]
     # index_list shape: [D, N]
     for idx, edge in enumerate(edges):
         edge = paddle.to_tensor(edge)
-        index_list.append(paddle.searchsorted(edge, reshaped_input[:, idx], right=True))
+        index_list.append(
+            paddle.searchsorted(edge, reshaped_input[:, idx], right=True)
+        )
     index_list = paddle.to_tensor(index_list)
     for i in _range(D):
-        on_edge = (reshaped_input[:, i] == edges[i][-1])
+        on_edge = reshaped_input[:, i] == edges[i][-1]
         if paddle.in_dynamic_mode():
             index_list[i][on_edge] -= 1
         else:
@@ -3920,21 +3928,23 @@ def __check_range(D, range):
     index_list = tuple(index_list)
     lut = paddle.arange(paddle.to_tensor(hist_shape).prod()).reshape(hist_shape)
     flattened_index = lut[index_list]
-    hist = paddle.bincount(flattened_index, reshaped_weights, minlength=paddle.to_tensor(hist_shape).prod())
+    hist = paddle.bincount(
+        flattened_index,
+        reshaped_weights,
+        minlength=paddle.to_tensor(hist_shape).prod(),
+    )
     hist = hist.reshape(hist_shape)
     hist = hist.astype('float32')
 
-    core = D*(slice(1, -1),)
+    core = D * (slice(1, -1),)
     hist = hist[core]
 
     if density:
         s = hist.sum()
         for i in _range(D):
-            shape = D*[1]
+            shape = D * [1]
             shape[i] = hist_shape[i] - 2
             hist = hist / dedges[i].reshape(shape)
         hist /= s
-
-    return (hist, edges)
-
 
+    return (hist, edges)