index_vector_dim = 2>,
          slice_sizes = array<i64: 1, 2, 2>,
          indices_are_sorted = false
        } : (tensor<3x4x2xf32>, tensor<2x3x2xi64>) -> tensor<2x3x2x2xf32>
        func.return %2 : tensor<2x3x2x2xf32>
      }
    }
  )mlir";
  OwningOpRef<ModuleOp> module_op = ParseModuleOpString(kModuleGather);
  ASSERT_TRUE(module_op);

  func::FuncOp main_fn = FindMainFuncOp(*module_op);

  func.return %rst : tensor<256x30x30x16xf32>

// CHECK-DAG: %[[CONSTANT:.*]] = arith.constant dense<0.000000e+00> : tensor<48xf32>
// CHECK-DAG: %[[CONSTANT0:.*]] = arith.constant dense<0.000000e+00> : tensor<1x3x3x48xf32>
// CHECK: %[[QUANTIZE:.*]] = "tfl.quantize"(%[[CONSTANT0]]) <{qtype = tensor<1x3x3x48x!quant.uniform<u8:f32, 1.000000e+00>>}>
// CHECK: %[[DEQUANTIZE:.*]] = "tfl.dequantize"(%[[QUANTIZE]])

    rhs_quantization_min_val = -128 : i64,
    rhs_quantization_max_val = 127 : i64
  } : (tensor<1x2x2x3xf32>, tensor<2x3x3x2x!tf_type.qint8>, tensor<f32>, tensor<i32>) -> tensor<1x3x3x2xf32>
  func.return %0 : tensor<1x3x3x2xf32>
}

//===----------------------------------------------------------------------===//
// tf.UniformQuantize and tf.UniformDequantize legalization

  // CHECK: %[[v1:.*]] = "tf.Transpose"(%arg1, %[[cst_1]]) : (tensor<2x4x7x3xf32>, tensor<4xi32>) -> tensor<2x7x3x4xf32>
  // CHECK: "tf.BatchMatMulV2"(%[[v0]], %[[v1]]) <{adj_x = false, adj_y = false}> : (tensor<2x7x5x3xf32>, tensor<2x7x3x4xf32>) -> tensor<2x7x5x4xf32>
}

func.func @einsum_matrixdotprod(%arg0: tensor<2x5x7x3xf32>, %arg1: tensor<7x3x4xf32>) -> tensor<2x5x4xf32> {

//
// ```
// %0 = stablehlo.constant dense<3>
// %1 = stablehlo.constant dense<4>
// %2 = stablehlo.constant dense<2>
// %3 = stablehlo.convolution(%%arg0, %%arg1) :
//          (tensor<?x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<?x3x4x2xf32>
// %4 = stablehlo.get_dimension_size %3, dim = 0 :
//          (tensor<?x3x4x2xf32>) -> tensor<i32>
// %5 = stablehlo.reshape %4 :
//          (tensor<i32>) -> tensor<1xi32>