}
      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>
      }
    }
    ```

used for communication with all hosts.}]>:$network_configs
  );

  let results = (outs);

  TF_DerivedOperandSizeAttr N = TF_DerivedOperandSizeAttr<0>;
}

def TF_ConvOp : TF_Op<"Conv", [Pure]> {
  let summary = [{
Computes a N-D convolution given (N+1+batch_dims)-D `input` and (N+2)-D `filter` tensors.
  }];

  let description = [{