maven { url "${mavenRepo.uri}" }
            }
            configurations {
                conf
                conf2
                conf3
                conf4
            }
            dependencies {
                conf 'org:a:1.0', 'org:b:1.0'
                conf2 'org:a:1.0', 'org:c:1.0'
                conf3 'org:b:1.0', 'org:c:1.0'
                conf4 'org:b:1.0', 'org:c:1.0', 'org:d:1.0'

	{"slicerunetostring", funcTag, 48},
	{"stringtoslicebyte", funcTag, 50},
	{"stringtoslicerune", funcTag, 53},
	{"slicecopy", funcTag, 54},
	{"decoderune", funcTag, 55},
	{"countrunes", funcTag, 56},
	{"convT", funcTag, 57},
	{"convTnoptr", funcTag, 57},
	{"convT16", funcTag, 59},
	{"convT32", funcTag, 61},
	{"convT64", funcTag, 62},
	{"convTstring", funcTag, 63},
	{"convTslice", funcTag, 66},
	{"assertE2I", funcTag, 67},

			withMounts(map[string]string{"/home/r2lib1": "/usr/r2lib1"}),
			withEnvs(map[string]string{"r2devices": "dev1 dev2"}),
		),
	)
	testManager.podDevices.insert("pod1", "con2", resourceName1,
		constructDevices([]string{"dev3"}),
		newContainerAllocateResponse(
			withDevices(map[string]string{"/dev/r1dev3": "/dev/r1dev3"}),
			withMounts(map[string]string{"/home/r1lib1": "/usr/r1lib1"}),
		),

    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"];
}

    ConstBoolAttrTrue, $asymmetric_quantize_inputs),
  [(HasRank<2> $input),
   (AreLastTwoDimsTransposed $perm_value),
   (IsBoolAttrEqual<"false"> $adj_y)]>;

// Replace conv-->transpose-->add with conv-->add-->transpose
// The bias needs only reshape (i.e. ReshapeNCHWBiasToNHWC) and not transpose
// because the bias's shape simply changes from NxCx1x1 to Nx1x1xC.
def ReorderNCHWTransposeAdd : Pat <

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 {

*   Keras:

    *   `tf.keras.layers.Conv` now includes a public `convolution_op` method.
        This method can be used to simplify the implementation of Conv
        subclasses. There are two primary ways to use this new method. The first
        is to use the method directly in your own `call` method: `python class
        StandardizedConv2D(tf.keras.layers.Conv2D): def call(self, inputs):

  // can let binary op to broadcast elements.
  if (elements_depth == 1) {
    return true;
  }

  // In TFLite Conv2D uses OHWI format for filter, and 1HWO for Depthwise Conv.
  // For conv:
  // Check if last dimension in filter equals the first dimension
  // For depthwise conv:
  // Check if the first in filter dimension equals the first dimension.
  if (filter_shape.empty() ||

  // CHECK: %[[dq:.*]] = "tfl.dequantize"(%[[q]])
  // CHECK: %[[conv:.*]] = "tfl.conv_2d"(%arg0, %[[dq]], %[[cst]])
  // CHECK: return %[[conv]] : tensor<256x8x7x3xf32>
}

// CHECK-LABEL: @fuseMulIntoFullyConnectedWithOptionalAttribute

      }
      func @_func(%input: tensor<2x112x112x12xf32>, %filter: tensor<7x7x3x64xf32>) {
        %filter_transform = "tf.Pad/tf.Transpose/tf.Reshape"(%filter): tensor<7x7x3x64xf32>) -> tensor<4x4x12x64xf32>
        %conv = "tf.Conv2D"(%input, %filter_transfrom) {strides = [1, 1, 1, 1]}: (tensor<2x112x112x12xf32>, tensor<4x4x12x64xf32>) -> tensor<2x112x112x64xf32>
      }
    }
    ```