lhs_dilation = dense<1> : tensor<1xi64>,
    padding = dense<0> : tensor<1x2xi64>,
    precision_config = [#mhlo<precision DEFAULT>, #mhlo<precision DEFAULT>],
    rhs_dilation = dense<1> : tensor<1xi64>,
    window_strides = dense<1> : tensor<1xi64>
  } : (tensor<16x32x256xbf16>, tensor<1x256x256xbf16>) -> tensor<16x32x256xbf16>
  func.return %0 : tensor<16x32x256xbf16>
}

// CHECK-LABEL:   func.func @convert_conv1d_dynamic_batch(

    }
    auto rhs_dilations_attr = rewriter.getNamedAttr(
        "rhs_dilation", GetI64ElementsAttr(rhs_dilations, &rewriter));

    auto window_strides_attr = rewriter.getNamedAttr(
        "window_strides", GetI64ElementsAttr(window_strides, &rewriter));

    auto dimension_numbers_attr = GetConvDimensionNumbersAttr(
        spatial_dim_indices, data_format, &rewriter);

    const int64_t input_channels =

  // CHECK: %[[INIT:.*]] = mhlo.constant dense<-2147483648> : tensor<i32>
  // CHECK: "mhlo.reduce_window"(%[[ARG]], %[[INIT]])
  // CHECK: <{window_dimensions = dense<[1, 2, 2, 1]> : tensor<4xi64>, window_strides = dense<[1, 4, 4, 1]> : tensor<4xi64>}>
  // CHECK: mhlo.maximum
  // CHECK: mhlo.return