// Adds `bias` to `value`.
Status BiasAdd(AbstractContext* ctx, AbstractTensorHandle* const value,
               AbstractTensorHandle* const bias, AbstractTensorHandle** output,
               const char* data_format = "NHWC", const char* name = nullptr,
               const char* raw_device_name = nullptr);

// The backward operation for "BiasAdd" on the "bias" tensor.
Status BiasAddGrad(AbstractContext* ctx,

    'NewFullyConnected',
    inputs=['input_: T', 'filter_: T', 'bias: T'],
    attrs=['act: {"", "RELU", "RELU6", "TANH"} = ""'],
    derived_attrs=['T: {float, int8}'],
    outputs=['o: T'])
def _composite_fully_connected(input_, filter_, bias, act):
  res = tf.raw_ops.MatMul(
      a=input_, b=filter_, transpose_a=False, transpose_b=True)
  res = tf.raw_ops.Add(x=res, y=bias)
  if act == 'RELU':
    return tf.raw_ops.Relu(features=res)

  func.return %1 : tensor<256x8x7x3xf32>

  // CHECK: %[[weight:.*]] = arith.constant dense<3.000000e+00> : tensor<3x3x3x3xf32>
  // CHECK: %[[bias:.*]] = arith.constant dense<[1.500000e+00, 3.000000e+00, 4.500000e+00]>
  // CHECK: %[[conv:.*]] = "tfl.conv_2d"(%arg0, %[[weight]], %[[bias]])
  // CHECK: return %[[conv]] : tensor<256x8x7x3xf32>

  }
}

// Creates a new `tfl.qconst` op for the bias. The bias values are 0s, because
// this bias a dummy bias (note that bias fusion is not considered for this
// transformation). The quantization scale for the bias is input scale *
// filter scale. `filter_const_op` is used to retrieve the filter scales and
// the size of the bias constant.
TFL::QConstOp CreateTflConstOpForDummyBias(

  func.func @serving_default(%arg0: tensor<!tf_type.resource<tensor<100x50xf32>>> {tf.resource_name = "dense/kernel"}, %arg1: tensor<!tf_type.resource<tensor<50xf32>>> {tf.resource_name = "dense/bias"}) -> (tensor<100x50xf32> {tf_saved_model.index_path = ["dense_2"]})
  attributes {tf.entry_function = {control_outputs = "", inputs = "", outputs = "dense_2/Add:0"}, tf_saved_model.exported_names = ["serving_default"]} {

}

// Op: BiasAdd()
// Summary: Adds `bias` to `value`.
//
// Description:
//   This is a special case of `tf.add` where `bias` is restricted to be 1-D.
//   Broadcasting is supported, so `value` may have any number of dimensions.
Status BiasAdd(AbstractContext* ctx, AbstractTensorHandle* const value,
               AbstractTensorHandle* const bias, AbstractTensorHandle** output,

// that also contains other supporting ops needed to construct the operands for
// the fused op. The caller provides the containing FuncOp as input with
// arguments specifying the input, weight, projection and bias.
// The weight, projection, bias and layer norm scale all need to be
// RankedTensorType.
// This class sets the layer norm coefficients to NoneType.
class ConvertLSTMCellSimpleToFusedLSTM {
 public:

		std.offset = -int(i.Bias) * 60
		l.cacheStart = alpha
		l.cacheEnd = omega
		l.cacheZone = std
		l.tx = make([]zoneTrans, 1)
		l.tx[0].when = l.cacheStart
		l.tx[0].index = 0
		return
	}

	// StandardBias must be ignored if StandardDate is not set,
	// so this computation is delayed until after the nzone==1
	// return above.
	std.offset = -int(i.Bias+i.StandardBias) * 60

	dst := &l.zone[1]

    }
    const int64_t cost_per_col = 2 * weight_type.getNumElements();

    *count = cost_per_col * cols;

    auto bias = op->getOperand(2);
    if (bias) {
      auto bias_type =
          mlir::dyn_cast_or_null<mlir::RankedTensorType>(bias.getType());
      if (bias_type && bias_type.hasStaticShape()) {
        *count += output_type.getNumElements();
      }
    }

    return true;
  }

	switch {
	case f == 0:
		return f, 0 // correctly return -0
	case IsInf(f, 0) || IsNaN(f):
		return f, 0
	}
	f, exp = normalize(f)
	x := Float64bits(f)
	exp += int((x>>shift)&mask) - bias + 1
	x &^= mask << shift
	x |= (-1 + bias) << shift
	frac = Float64frombits(x)
	return