fix sample method of Uniform and Normal class (#26713)

* fix sample shape error

* Add unittest

* change assert_allclose to assert_equal

* Add unittest doc

* fix encoding problem

python/paddle/distribution.py

@@ -243,10 +243,19 @@ class Uniform(Distribution):
             zero_tmp = tensor.fill_constant_batch_size_like(
                 self.low + self.high, batch_shape + shape, self.low.dtype, 0.)
             uniform_random_tmp = nn.uniform_random_batch_size_like(
-                zero_tmp, zero_tmp.shape, min=0., max=1., seed=seed)
-            output = uniform_random_tmp * (zero_tmp + self.high - self.low
-                                           ) + self.low
-            return nn.reshape(output, output_shape, name=name)
+                zero_tmp,
+                zero_tmp.shape,
+                dtype=convert_dtype(zero_tmp.dtype),
+                min=0.,
+                max=1.,
+                seed=seed)
+            zero_tmp_reshape = nn.reshape(zero_tmp, output_shape)
+            uniform_random_tmp_reshape = nn.reshape(uniform_random_tmp,
+                                                    output_shape)
+            output = uniform_random_tmp_reshape * (
+                zero_tmp_reshape + self.high - self.low)
+            output = elementwise_add(output, self.low, name=name)
+            return output
         else:
             output_shape = shape + batch_shape
             output = nn.uniform_random(
@@ -446,11 +455,17 @@ class Normal(Distribution):
             output_shape = shape + batch_shape
             zero_tmp = tensor.fill_constant_batch_size_like(
                 self.loc + self.scale, batch_shape + shape, self.loc.dtype, 0.)
-            zero_tmp_shape = nn.shape(zero_tmp)
+            zero_tmp_reshape = nn.reshape(zero_tmp, output_shape)
+            zero_tmp_shape = nn.shape(zero_tmp_reshape)
             normal_random_tmp = nn.gaussian_random(
-                zero_tmp_shape, mean=0., std=1., seed=seed)
-            output = normal_random_tmp * (zero_tmp + self.scale) + self.loc
-            return nn.reshape(output, output_shape, name=name)
+                zero_tmp_shape,
+                mean=0.,
+                std=1.,
+                seed=seed,
+                dtype=convert_dtype(self.loc.dtype))
+            output = normal_random_tmp * (zero_tmp_reshape + self.scale)
+            output = elementwise_add(output, self.loc, name=name)
+            return output
         else:
             output_shape = shape + batch_shape
             output = nn.gaussian_random(output_shape, mean=0., std=1., seed=seed) * \