%4 = "tfl.dequantize"(%3) : (tensor<32x12x!quant.uniform<u8<1:255>:f32, 0.021826678373682216:151>>) -> tensor<32x12xf32>
  %5 = "tfl.fully_connected"(%2, %4, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<1x224x224x3xf32>, tensor<32x12xf32>, tensor<32xf32>) -> tensor<1x112x112x32xf32>

  %5 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<128x128xf32>} : () -> tensor<128x128xf32>
  %6 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<128xf32>} : () -> tensor<128xf32>
  %7 = "tfl.fully_connected"(%4, %5, %6) {tac.device = "CPU", tac.inference_type = "FLOAT", fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<128x128xf32>, tensor<128x128xf32>, tensor<128xf32>) -> tensor<128x128xf32>

    std::vector<int> GetQuantizableOperandIndices() { return {1}; }
  }];
}

// TODO(jpienaar): Update post discussion on semantics of FC OP.
def TFL_FullyConnectedOp : TFL_Op<"fully_connected", [
    Pure, AccumulatorUniformScale<2, 0, 1>,
    AffineQuantizedOpInterface,
    AffineOpCoefficient<0, 1>,
    TFL_SparseOp,
    DeclareOpInterfaceMethods<TFL_ArithmeticCount>,
    QuantizableResult,

    llvm::cl::init(""));

// NOLINTNEXTLINE
opt<bool> unfold_batchmatmul(
    "unfold_batchmatmul",
    llvm::cl::desc(
        "Whether to unfold TF BatchMatMul to a set of TFL FullyConnected ops."),
    llvm::cl::init(false));

// NOLINTNEXTLINE
opt<bool> unfold_large_splat_constant(
    "unfold-large-splat-constant",
    llvm::cl::desc("Whether to unfold large splat constant tensors to reduce "

  } else {
    result.can_compress = false;
    return result;
  }

  // Currently we only support compressing weights of ops:
  //   Conv, DepthwiseConv, TransposeConv, whose filter has rank 4, and
  //   FullyConnected, whose filter has rank 2.
  if (type.getRank() != 2 && type.getRank() != 4) {
    result.can_compress = false;
    return result;
  }

  float random_sparsity = CalculateRandomSparsity(attr, type);

def LegalizeDiv : Pat<(TF_DivOp $lhs, $rhs),
                      (TFL_DivOp $lhs, $rhs, TFL_AF_None)>;

// When batch size is known, TF BatchMatMul gets unfolded to TFL FullyConnected
// with additional ops. In the case of unknown batch size, the match will
// fall through to here and convert to TF Lite BatchMatMul.
// TODO(b/207064634): CreateEmptyBoolAttr is a temporary workaround for this bug.

    "ADD",
    "AVERAGE_POOL_2D",
    "CONCATENATION",
    "CONV_2D",
    "DEPTHWISE_CONV_2D",
    "DEPTH_TO_SPACE",
    "DEQUANTIZE",
    "EMBEDDING_LOOKUP",
    "FLOOR",
    "FULLY_CONNECTED",
    "HASHTABLE_LOOKUP",
    "L2_NORMALIZATION",
    "L2_POOL_2D",
    "LOCAL_RESPONSE_NORMALIZATION",
    "LOGISTIC",
    "LSH_PROJECTION",
    "LSTM",
    "MAX_POOL_2D",
    "MUL",

    modified to return nan when they have seen less or equal to 1 unit of
    weight.
*   Adds time series models to contrib. See contrib/timeseries/README.md for
    details.
*   Adds FULLY_CONNECTED Op to tensorflow/lite/schema.fbs

## Known Issues

*   Tensorflow_gpu compilation fails with Bazel 0.5.3.

## Bug Fixes and Other Changes