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(
            [

      dilations: Sequence[int] = (1, 1, 1, 1),
      padding: str = 'SAME',
  ):
    class DepthwiseConvModel(module.Module):
      """A simple model with a single depthwise conv2d, bias and relu."""

      def __init__(self):
        self.out_channel_size = filter_shape[2] * filter_shape[3]

        # 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

  %5 = "quantfork.stats"(%4) {layerStats = dense<[-56.2916565, 122.922478]> : tensor<2xf32>} : (tensor<1x4xf32>) -> tensor<1x4xf32>
  %6 = "tfl.svdf"(%0, %1, %2, %3, %5) {fused_activation_function = "RELU", rank = 1 : i32} : (tensor<1x3xf32>, tensor<2x3xf32>, tensor<2x1xf32>, tensor<2xf32>, tensor<1x4xf32>) -> tensor<1x2xf32>

	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

    "tf.Yield"(%t0, %t1, %t2) : (tensor<2xf32>, tensor<2xf32>, tensor<2xf32>) -> ()
    }, {
     %e0 = "tf.Neg"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>
     %e1 = "tf.Relu"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>
     %e2 = "tf.Sin"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>
     "tf.Yield"(%e0, %e1, %e2) : (tensor<2xf32>, tensor<2xf32>, tensor<2xf32>) -> ()

//===----------------------------------------------------------------------===//

OpFoldResult LeakyReluOp::fold(FoldAdaptor adaptor) {
  auto operands = adaptor.getOperands();
  assert(operands.size() == 1 && "leaky relu has one operand");

  // leaky_relu(x, alpha: 1) -> x
  if (getAlpha().convertToFloat() == 1.0f &&
      getOperand().getType() == getType())
    return getOperand();

    to matrix multiplication and convolution, these building blocks include:
    Direct batched convolution Pooling: maximum, minimum, average Normalization:
    LRN, batch normalization Activation: rectified linear unit (ReLU) Data
    manipulation: multi-dimensional transposition (conversion), split, concat,
    sum and scale.

*   TensorForest Estimator now supports SavedModel export for serving.