def custom_scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None):
    original_dtype = x.dtype
    if original_dtype in [paddle.int16,paddle.int8, paddle.uint8]:
        x = paddle.cast(x, dtype=paddle.int32)
    
    # ToDO:May need to add logic when scale_input is tensor
    # because `x` dtype and `scale` dtype need to match!
    if not isinstance(scale, paddle.Tensor):
        scale = paddle.to_tensor(scale, dtype=x.dtype)
        
    if not isinstance(bias, paddle.Tensor):
        bias = paddle.to_tensor(bias, dtype=x.dtype)
    
    # Convert dtype to int32 for calculations if input dtype is int8 or uint8
    # because add or multiply API don't support them at-least for device=CPU 

        
    if bias_after_scale:
        out = paddle.add(paddle.multiply(x, scale), bias)
    else:
        out = paddle.multiply(paddle.add(x, bias), scale)
    
    # Truncate to the nearest integer for integer types before casting back
    # if "int" in str(original_dtype):
    #     out = paddle.trunc(out)
    # Turns out this dosen't work, CurseYou Paddlefokls!!
    
    # Cast back to the original dtype
    out = paddle.cast(out, dtype=original_dtype)
    
    
  # Apply activation function if provided and allowed
    if act is not None:
        if original_dtype not in [paddle.float32, paddle.float64]:
            print("Error: For CPU, only float32 and float64 are allowed for act =", act)
        else:
            # These where the ones mentioned in docs for paddle.scale
            # Reference: https://www.paddlepaddle.org.cn/documentation/docs/en/api/paddle/scale_en.html#scale 
            allowed_acts = ['tanh', 'softmax', 'sigmoid', 'relu']
            if act in allowed_acts:
                if hasattr(paddle.nn.functional, act):
                    out = getattr(paddle.nn.functional, act)(out)
                elif hasattr(paddle, act):
                    out = getattr(paddle, act)(out)
                else:
                    print("Error: Unsupported activation function. Supported functions are:", allowed_acts)
            else:
                print("Error: Unsupported activation function. Supported functions are:", allowed_acts)

    return out