const auto float_graph = model_->subgraphs()->Get(subgraph_idx);
    ASSERT_EQ(quantized_graph->tensors()->size(),
              float_graph->tensors()->size());
    // Make sure the graph only has one Conv operation.
    ASSERT_EQ(quantized_graph->operators()->size(), 1);
    const auto op = quantized_graph->operators()->Get(0);
    const uint32_t op_code_idx = op->opcode_index();

				conv = conv1
			}
		}
		if Arch.LinkArch.Family == sys.ARM64 || Arch.LinkArch.Family == sys.Wasm || Arch.LinkArch.Family == sys.S390X || s.softFloat {
			if conv1, ok1 := uint64fpConvOpToSSA[twoTypes{s.concreteEtype(ft), s.concreteEtype(tt)}]; ok1 {
				conv = conv1
			}
		}

		if Arch.LinkArch.Family == sys.MIPS && !s.softFloat {
			if ft.Size() == 4 && ft.IsInteger() && !ft.IsSigned() {

};

using ConvertConv2DDynamic =
    ConvertConvDynamic<TF::Conv2DOp, /*num_spatial_dims=*/2>;

// Converts the TensorFlow conv op in template to the generic HLO conv op by
// converting TensorFlow op attributes to HLO op attributes.
//
// Sample result for Conv2D:
//
//   %conv = "mhlo.convolution"(%input, %filter) {
//     strides = [1, 2],
//     paddings = [[1, 0], [1, 1]],
//     ...
//   }
//

      }
      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>
      }
    }
    ```

    *   `tf.data.experimental.service.register_dataset` now accepts optional
        `compression` argument.

*   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

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 = [{