parameters: (
      inp: Tensor,
      weight: Tensor,
      bias: Tensor,
      dtype=None,
      stride: Union[int, Tuple[int, int]] = 1,
      padding: Union[int, Tuple[int, int]] = 0,
      dilation: Union[int, Tuple[int, int]] = 1,
      groups: int = 1,
      nonlinear_mode="identity",
      conv_mode="cross_correlation",
      compute_mode="default",
  )
return_statement: return outputs
docstring: r"""Convolution bias with activation operation, only for inference.
  
      Args:
          inp: feature map of the convolution operation.
          weight: convolution kernel.
          bias: bias added to the result of convolution
          stride: stride of the 2D convolution operation. Default: 1
          padding: size of the paddings added to the input on both sides
              of its spatial dimensions. Only zero-padding is supported. Default: 0
          dilation: dilation of the 2D convolution operation. Default: 1
          groups: number of groups into which the input and output channels are divided,
              so as to perform a "grouped convolution". When ``groups`` is not 1,
              ``in_channels`` and ``out_channels`` must be divisible by ``groups``,
              and the shape of weight should be `(groups, out_channel // groups,
              in_channels // groups, height, width)`.
          conv_mode: supports 'cross_correlation' or 'convolution'. Default:
              'cross_correlation'
          dtype: support for ``np.dtype``, Default: np.int8
          compute_mode: when set to "default", no special requirements will be
              placed on the precision of intermediate results. When set to "float32",
              "float32" would be used for accumulator and intermediate result,
              but only effective when input and output are of float16 dtype.
docstring_summary: r"""Convolution bias with activation operation, only for inference.
docstring_tokens: r Convolution bias with activation operation only for inference .
function: def conv_bias_activation(
      inp: Tensor,
      weight: Tensor,
      bias: Tensor,
      dtype=None,
      stride: Union[int, Tuple[int, int]] = 1,
      padding: Union[int, Tuple[int, int]] = 0,
      dilation: Union[int, Tuple[int, int]] = 1,
      groups: int = 1,
      nonlinear_mode="identity",
      conv_mode="cross_correlation",
      compute_mode="default",
  ) -> Tensor:
      r"""Convolution bias with activation operation, only for inference.
  
      Args:
          inp: feature map of the convolution operation.
          weight: convolution kernel.
          bias: bias added to the result of convolution
          stride: stride of the 2D convolution operation. Default: 1
          padding: size of the paddings added to the input on both sides
              of its spatial dimensions. Only zero-padding is supported. Default: 0
          dilation: dilation of the 2D convolution operation. Default: 1
          groups: number of groups into which the input and output channels are divided,
              so as to perform a "grouped convolution". When ``groups`` is not 1,
              ``in_channels`` and ``out_channels`` must be divisible by ``groups``,
              and the shape of weight should be `(groups, out_channel // groups,
              in_channels // groups, height, width)`.
          conv_mode: supports 'cross_correlation' or 'convolution'. Default:
              'cross_correlation'
          dtype: support for ``np.dtype``, Default: np.int8
          compute_mode: when set to "default", no special requirements will be
              placed on the precision of intermediate results. When set to "float32",
              "float32" would be used for accumulator and intermediate result,
              but only effective when input and output are of float16 dtype.
      """
      ph, pw = _pair(padding)
      sh, sw = _pair_nonzero(stride)
      dh, dw = _pair_nonzero(dilation)
      sparse_type = "dense" if groups == 1 else "group"
      compute_mode = _config._get_actual_op_param(compute_mode, _config.__compute_mode)
      conv_format = _config._get_actual_op_param("NCHW", _config.__conv_format)
      op = builtin.ConvBias(
          stride_h=sh,
          stride_w=sw,
          pad_h=ph,
          pad_w=pw,
          dilate_h=dh,
          dilate_w=dw,
          dtype=dtype,
          format=conv_format,
          strategy=get_execution_strategy(),
          nonlineMode=nonlinear_mode,
          mode=conv_mode,
          compute_mode=compute_mode,
          sparse=sparse_type,
      )
      (outputs,) = apply(op, inp, weight, bias)
      return outputs
function_tokens: def conv_bias_activation ( inp : Tensor , weight : Tensor , bias : Tensor , dtype = None , stride : Union [ int , Tuple [ int , int ] ] = 1 , padding : Union [ int , Tuple [ int , int ] ] = 0 , dilation : Union [ int , Tuple [ int , int ] ] = 1 , groups : int = 1 , nonlinear_mode = "identity" , conv_mode = "cross_correlation" , compute_mode = "default" , ) -> Tensor : ph , pw = _pair ( padding ) sh , sw = _pair_nonzero ( stride ) dh , dw = _pair_nonzero ( dilation ) sparse_type = "dense" if groups == 1 else "group" compute_mode = _config . _get_actual_op_param ( compute_mode , _config . __compute_mode ) conv_format = _config . _get_actual_op_param ( "NCHW" , _config . __conv_format ) op = builtin . ConvBias ( stride_h = sh , stride_w = sw , pad_h = ph , pad_w = pw , dilate_h = dh , dilate_w = dw , dtype = dtype , format = conv_format , strategy = get_execution_strategy ( ) , nonlineMode = nonlinear_mode , mode = conv_mode , compute_mode = compute_mode , sparse = sparse_type , ) ( outputs , ) = apply ( op , inp , weight , bias ) return outputs