From fb993e06dc4e71b413dea89dc02da0df632f2873 Mon Sep 17 00:00:00 2001
From: lvyufeng <lvyufeng@cqu.edu.cn>
Date: Mon, 21 Jul 2025 12:34:29 +0800
Subject: [PATCH] fix e class

---
 mindnlp/core/_tensor.py       | 19 ++++++++++++++++++-
 mindnlp/core/nn/functional.py |  1 -
 2 files changed, 18 insertions(+), 2 deletions(-)

diff --git a/mindnlp/core/_tensor.py b/mindnlp/core/_tensor.py
index 505867497..a654c3d3e 100644
--- a/mindnlp/core/_tensor.py
+++ b/mindnlp/core/_tensor.py
@@ -1,6 +1,6 @@
 import math
 import numpy as np
-from functools import partial
+import warnings
 import mindspore
 from mindspore import Tensor
 from mindspore.common.tensor import _TensorMeta
@@ -379,6 +379,8 @@ def unfold(self, dimension, size, step):
     StubTensor.unfold = unfold
 
     def new(self, *shape):
+        if not isinstance(shape[0], int):
+            return tensor(shape[0], dtype=self.dtype)
         return ops.empty(*shape, dtype=self.dtype)
 
     Tensor.new = new
@@ -540,6 +542,21 @@ def new_tensor(self, data, *, dtype=None, device=None, requires_grad=False, layo
     Tensor.triu_ = ops.inplace_triu
     StubTensor.triu_ = ops.inplace_triu
 
+    @property
+    def real(self):
+        return ops.real(self)
+    
+    Tensor.real = real
+    StubTensor.real = real
+
+    def bfloat16(self):
+        if ON_A1:
+            warnings.warn('910A do not support bfloat16, use float16 instead.')
+            return self.to(_dtype.float16)
+        return self.to(_dtype.bfloat16)
+
+    Tensor.bfloat16 = bfloat16
+    StubTensor.bfloat16 = bfloat16
 
 def _rebuild_from_type_v2(func, new_type, args, state):
     ret = func(*args)
diff --git a/mindnlp/core/nn/functional.py b/mindnlp/core/nn/functional.py
index fa66b06af..497fca22c 100644
--- a/mindnlp/core/nn/functional.py
+++ b/mindnlp/core/nn/functional.py
@@ -166,7 +166,6 @@ def avg_pool2d(input, kernel_size, stride=None, padding=0, ceil_mode=False, coun
     Returns:
     - numpy array: The result of the average pooling operation.
     """
-    print(kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override)
     if use_pyboost():
         return mint.nn.functional.avg_pool2d(input, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override)