Option<"is_signed_", "is-signed", "bool", "false",
               "Is the corresponding integer signed">,
  ];
}

def IdentifyDilatedConvPass : Pass<"tfl-identify-dilated-conv", "mlir::func::FuncOp"> {
  let summary = "Convert dense tensor to sparse format.";
  let constructor = "CreateIdentifyDilatedConvPass()";
  let dependentDialects = ["TFL::TensorFlowLiteDialect"];
}

  // TFR_ConstantTensorOp (
  //   ConstantOp (ConstAttr<F32Attr (in_scale[0] * in_scale[1] /
  //   out_scale))
  // )
  // Currently, all decompositions using this pattern (Conv2D, FC) have the
  // following preconditions:
  // * out_scale: float scalar attribute
  // * in_scale[0] (input scale): float scalar, given by tf.Const -> tfr.cast
  // * in_scale[1] (filter scale): float scalar/vector

    quantization_axis = -1 : i64, quantization_min_val = -128 : i64,
    quantization_max_val = 127 : i64
  } : (
    tensor<3x3x10x20x!tf_type.qint8>, tensor<f32>, tensor<i32>
  ) -> tensor<3x3x10x20xf32>
  %0 = "tf.Conv2D"(%input, %filter_new) {
    Tin = "tfdtype$DT_FLOAT", Tout = "tfdtype$DT_FLOAT",
    attr_map = "", batch_group_count = 1 : i64,
    explicit_padding = [], feature_group_count = 1 : i64, lhs_dilation = [1, 1],

table Conv2DOptions {
  padding:Padding;
  stride_w:int;
  stride_h:int;
  fused_activation_function:ActivationFunctionType;
  dilation_w_factor:int = 1;
  dilation_h_factor:int = 1;
  // Parameters for Conv2D version 8 or above.
  // When set, quantized_bias_type defines the dtype for both bias and accumulator.
  quantized_bias_type: TensorType;
}

// Options for both Conv3D and Conv3DTranspose.
table Conv3DOptions {

			return false
		}
	}

	// DefaultLit is necessary for non-constants too: n might be 1.1<<k.
	n = DefaultLit(n, types.Types[types.TINT])
	*np = n

	return true
}

func Conv(n ir.Node, t *types.Type) ir.Node {
	if types.IdenticalStrict(n.Type(), t) {
		return n
	}
	n = ir.NewConvExpr(base.Pos, ir.OCONV, nil, n)
	n.SetType(t)
	n = Expr(n)
	return n
}