]
    )
    def __call__(self, input_tensor: core.Tensor) -> Mapping[str, core.Tensor]:
      """Performs a matrix multiplication.

      Args:
        input_tensor: Input tensor to matmul with the filter.

      Returns:
        A map of: output key -> output result.
      """

      out = math_ops.matmul(input_tensor, self.filters)
      return {'output': out}

  if (input_tensor.isSplat()) {
    return input_tensor.reshape(input_tensor.getType().cloneWith(
        output_shape, input_tensor.getElementType()));
  }

  // MLIR implementation pads elements < 8 bits to 8 bits and pads non byte
  // aligned to the nearest byte. So this is allowed.
  const char* raw_input = input_tensor.getRawData().data();
  const int element_byte_size =

// "third_party/tensorflow/compiler/xla/xla_data.pb.h" into
// "third_party/tensorflow/compiler/mlir/tensorflow/ir/tf_ops_n_z.cc" is
// resolved
LogicalResult PrecheckForXlaConvV2Op(XlaConvV2Op op) {
  auto input_tensor = op.getLhs();
  auto kernel_tensor = op.getRhs();
  auto window_strides = op.getWindowStrides();
  auto padding = op.getPadding();
  auto lhs_dilation = op.getLhsDilation();

    QuantizableResult,
    Pure]> {
  let summary = "Mean operator";

  let description = [{
    Computes the mean of elements across dimensions of a tensor.
    Reduces input_tensor along the dimensions given in axis.
    Unless keepdims is true, the rank of the tensor is reduced by 1 for
    each entry in axis. If keepdims is true, the reduced dimensions are retained
    with length 1.
  }];