code: def conv(
      lhs,
      rhs,
      window_strides,
      padding,
      lhs_dilation,
      rhs_dilation,
      dimension_numbers,
      feature_group_count=1,
      precision_config=None,
      preferred_element_type=None,
      name=None,
      use_v2=False,
      batch_group_count=1,
  ):
      """Wraps the XLA ConvGeneralDilated operator.
  
      ConvGeneralDilated is the most general form of XLA convolution and is
      documented at
      https://www.tensorflow.org/performance/xla/operation_semantics#conv_convolution
  
      Args:
        lhs: the input tensor
        rhs: the kernel tensor
        window_strides: the inter-window strides
        padding: the padding to apply at the start and end of each input dimensions
        lhs_dilation: dilation to apply between input elements
        rhs_dilation: dilation to apply between kernel elements
        dimension_numbers: a `ConvolutionDimensionNumbers` proto.
        feature_group_count: number of feature groups for grouped convolution.
        precision_config: a `xla.PrecisionConfig` proto.
        preferred_element_type: the result `dtype`.
        name: an optional name for the operator.
        use_v2: an optional request to use the XlaConvV2 op even if not necessary.
        batch_group_count: number of batch groups or grouped filters.
  
      Returns:
        A tensor representing the output of the convolution.
      """
      precision_config_proto = ""
      if precision_config:
          precision_config_proto = precision_config.SerializeToString()
      needs_v2 = (
          preferred_element_type
          or (lhs.dtype != rhs.dtype)
          or batch_group_count > 1
      )
      if preferred_element_type is None:
          preferred_element_type = np_utils.result_type(
              lhs.dtype, rhs.dtype
          )
      if needs_v2 or use_v2:
          return gen_xla_ops.xla_conv_v2(
              lhs,
              rhs,
              window_strides=window_strides,
              padding=padding,
              lhs_dilation=lhs_dilation,
              rhs_dilation=rhs_dilation,
              feature_group_count=feature_group_count,
              batch_group_count=batch_group_count,
              dimension_numbers=dimension_numbers.SerializeToString(),
              precision_config=precision_config_proto,
              preferred_element_type=preferred_element_type,
              name=name,
          )
      return gen_xla_ops.xla_conv(
          lhs,
          rhs,
          window_strides=window_strides,
          padding=padding,
          lhs_dilation=lhs_dilation,
          rhs_dilation=rhs_dilation,
          feature_group_count=feature_group_count,
          dimension_numbers=dimension_numbers.SerializeToString(),
          precision_config=precision_config_proto,
          name=name,
      )
  
imports: from tensorflow.compiler.tf2xla.ops import gen_xla_ops
  from local_xla.xla import xla_data_pb2
  from tensorflow.core.framework import attr_value_pb2
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import bitwise_ops
  from tensorflow.python.ops import gen_math_ops
  from tensorflow.python.ops import gen_random_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import random_ops_util
  from tensorflow.python.ops import special_math_ops
  from tensorflow.python.ops.numpy_ops import np_utils
label_desc: Use image node_id 8 for calling a global function with example usage: conv(lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation, dimension_numbers, feature_group_count, precision_config, preferred_element_type, name, use_v2, batch_group_count) and returns: gen_xla_ops, gen_xla_ops