data_format = "NHWC", dilations = [1, 1, 1, 1], epsilon = 9.99999974E-5 : f32, explicit_paddings = [], filter_format = "HWIO", fused_ops = ["BiasAdd", "Relu"], leakyrelu_alpha = 2.000000e-01 : f32, num_args = 2 : i64, operandSegmentSizes = array<i32: 1, 1, 2, 2>, padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true

      padding: str = 'SAME',
      has_func_alias: bool = False,
  ) -> module.Module:
    class ConvModel(module.Module):
      """A simple model with a single conv2d, bias and relu."""

      def __init__(self):
        self.out_channel_size = filter_shape[-1]

        # This ensures filters will have different value range per out channel
        self.filters = np.stack(
            [

  y = _tfr_quant_raw_data(x)
  s, z = _tfr_quant_qparam(x)
  s = _tfr_quant_scale_factor(1.0, [s, s])
  s = _tfr_quant_scale_factor(1.0, [s])
  y = math_ops.Sub(y, z)
  qmin, qmax = _tfr_quant_act_range('RELU', 1.0, 0)
  (qmin, qmax)  # pylint: disable=pointless-statement
  d = _tfr_quant_rescale(y, s, 0)
  e = math_ops.Cast(x=d, DstT=dtypes.int16)
  f = math_ops.Cast(x=e, DstT=dtypes.int8)
  return f

	xArgRM64         // arg r/m64
	xArgRM8          // arg r/m8
	xArgReg          // arg reg
	xArgRegM16       // arg reg/m16
	xArgRegM32       // arg reg/m32
	xArgRegM8        // arg reg/m8
	xArgRel16        // arg rel16
	xArgRel32        // arg rel32
	xArgRel8         // arg rel8
	xArgSS           // arg SS
	xArgST           // arg ST, aka ST(0)
	xArgSTi          // arg ST(i) with +i in opcode
	xArgSreg         // arg Sreg