code: def testConvFollowedByEinsumWithGradient(
      self,
      conv_op,
      input_size,
      kernel_size,
      einsum_eq,
      strides,
      input_sharding,
      padding,
  ):
      x_in = constant_op.constant(
          np.random.random(size=input_size), dtype=dtypes.float32
      )
      kernel_in = constant_op.constant(
          np.random.random(size=kernel_size), dtype=dtypes.float32
      )
      weight = constant_op.constant(
          np.random.random(size=(2, 2)), dtype=dtypes.float32
      )
  
      @polymorphic_function.function
      def conv_fn(inputs, img_kernel, layer_weights):
          with backprop.GradientTape() as tape:
              tape.watch([inputs, img_kernel, layer_weights])
              output = conv_op(
                  inputs, img_kernel, strides=strides, padding=padding
              )
              output = special_math_ops.einsum(
                  einsum_eq, output, layer_weights
              )
  
          inputs_grad, kernel_grad, weight_grad = tape.gradient(
              output, [inputs, img_kernel, layer_weights]
          )
          return output, inputs_grad, kernel_grad, weight_grad
  
      result, inputs_grad, kernel_grad, weight_grad = conv_fn(
          x_in, kernel_in, weight
      )
  
      if input_sharding == "replicated":
          input_layout = Layout.replicated(self.mesh, len(input_size))
          output_layout = self.replicated_2d
      elif input_sharding == "batch_sharded":
          input_layout = Layout.batch_sharded(
              self.mesh, BATCH_DIM, len(input_size)
          )
          output_layout = self.batch_sharded_2d
  
      kernel_layout = Layout.replicated(self.mesh, len(kernel_size))
  
      d_x_in = numpy_util.pack_numpy(x_in, input_layout)
      d_kernel_in = numpy_util.pack_numpy(kernel_in, kernel_layout)
      d_weight = numpy_util.pack_numpy(weight, self.replicated_2d)
      d_result, d_inputs_grad, d_kernel_grad, d_weight_grad = conv_fn(
          d_x_in, d_kernel_in, d_weight
      )
  
      self.assertDTensorEqual(result, output_layout, d_result)
      # TODO(b/208700444): layout of input grads should match layout of input.
      self.assertDTensorEqual(
          inputs_grad,
          Layout.replicated(self.mesh, len(input_size)),
          d_inputs_grad,
      )
      self.assertDTensorEqual(kernel_grad, kernel_layout, d_kernel_grad)
      self.assertDTensorEqual(
          weight_grad, self.replicated_2d, d_weight_grad
      )
  
imports: from absl.testing import parameterized
  import numpy
  from tensorflow.dtensor.python import layout
  from tensorflow.dtensor.python import numpy_util
  from tensorflow.dtensor.python.tests import test_util
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager.polymorphic_function import polymorphic_function
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import special_math_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 3 for calling the ConvOpTest obj's underlying member method code with example usage: obj.testConvFollowedByEinsumWithGradient(conv_op, input_size, kernel_size, einsum_eq, strides, input_sharding, padding) and returns: output,  inputs_grad,  kernel_grad,  weight_grad