code: def testConvFollowedByEinsum(
      self,
      conv_op,
      input_size,
      kernel_size,
      einsum_eq,
      strides,
      eager_mode,
      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
      )
  
      def conv_fn(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
          )
          return output
  
      if not eager_mode:
          conv_fn = polymorphic_function.function(conv_fn)
  
      golden_result = 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 = conv_fn(d_x_in, d_kernel_in, d_weight)
  
      self.assertDTensorEqual(golden_result, output_layout, d_result)
  
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 2 for calling the ConvOpTest obj's underlying member method code with example usage: obj.testConvFollowedByEinsum(conv_op, input_size, kernel_size, einsum_eq, strides, eager_mode, input_sharding, padding) and returns: output