bias_add:
    description: |
        Returns the sum of the input Tensor and the bias Tensor. Before adding, the bias Tensor will be broadcasted to be
        consistent with the shape of the input Tensor.
    
        Args:
            data_format (str, optional): The format of input and output data.
                It should be ``"NHWC"`` , ``"NCHW"`` or ``"NCDHW"`` .
                Default is ``"NCHW"`` .
    
        Inputs:
            - **input_x** (Tensor) - The input tensor. The shape can be 2-5 dimensions. Supported dtypes:
    
              - Ascend/CPU: all Number type.
              - GPU: float16, float32, int8.
    
            - **bias** (Tensor) - The bias tensor, with shape :math:`(C)`. C must be the same as channel dimension C of
              `input_x`. It has the same type as `input_x`.
    
        Outputs:
            Tensor, with the same shape and data type as `input_x`.
    
        Raises:
            TypeError: If `data_format` is not a str.
            ValueError: If value of `data_format` is not in the range of ['NHWC','NCHW','NCDHW'].
            TypeError: If `input_x` or `bias` is not a Tensor.
            TypeError: If dtype of `input_x` or `bias` is inconsistent.
            TypeError: If dimension of `input_x` is not in the range [2, 5].
    
        Supported Platforms:
            ``Ascend`` ``GPU`` ``CPU``
    
        Examples:
            >>> import mindspore
            >>> import numpy as np
            >>> from mindspore import Tensor, ops
            >>> input_x = Tensor(np.arange(6).reshape((2, 3)), mindspore.float32)
            >>> bias = Tensor(np.random.random(3).reshape((3,)), mindspore.float32)
            >>> bias_add = ops.BiasAdd()
            >>> output = bias_add(input_x, bias)
            >>> print(output.shape)
            (2, 3)