%control_11 = tf_executor.island wraps "tf.LoadTPUEmbeddingAdagradParameters"(%outputs_2, %outputs_5) {config = "", num_shards = 1 : i64, shard_id = 0 : i64, table_id = -1 : i64, table_name = "table4"} : (tensor<8xf32>, tensor<8xf32>) -> ()
    tf_executor.fetch
  }
  return
}

     "tf.LoadTPUEmbeddingAdagradParameters"(%0, %1) {config = "", num_shards = 1 : i64, shard_id = 0 : i64, table_id = -1 : i64, table_name = "table1"} : (tensor<8xf32>, tensor<8xf32>) -> ()
     %3 = "tf.ReadVariableOp"(%arg3) {device = ""} : (tensor<*x!tf_type.resource<tensor<8xf32>>>) -> tensor<8xf32>

     "tf.LoadTPUEmbeddingAdagradParameters"(%0, %1) {config = "", num_shards = 1 : i64, shard_id = 0 : i64, table_id = -1 : i64, table_name = "table1"} : (tensor<8xf32>, tensor<8xf32>) -> ()
     %3 = "tf.ReadVariableOp"(%arg3) {device = ""} : (tensor<*x!tf_type.resource<tensor<8xf32>>>) -> tensor<8xf32>

        input_arg {
          name: "basename"
          type: DT_STRING
        }
        input_arg {
          name: "shard"
          type: DT_INT32
        }
        input_arg {
          name: "num_shards"
          type: DT_INT32
        }
        output_arg {
          name: "filename"
          type: DT_STRING
        }
      }
      op {
        name: "StringJoin"
        input_arg {

Generate a sharded filename. The filename is printf formatted as
  }];

  let description = [{
%s-%05d-of-%05d, basename, shard, num_shards.
  }];

  let arguments = (ins
    TF_StrTensor:$basename,
    TF_Int32Tensor:$shard,
    TF_Int32Tensor:$num_shards
  );

  let results = (outs
    TF_StrTensor:$filename
  );
}

        // expected-remark@above {{ID: 0}}
        // expected-remark@above {{Successors: {3}}}
        "tf.LoadTPUEmbeddingAdagradParameters"(%arg1, %arg2) {config = "", num_shards = 1 : i64, shard_id = 0 : i64, table_id = -1 : i64, table_name = "table1"} : (tensor<8xf32>, tensor<8xf32>) -> ()
        // expected-remark@above {{ID: 1}}
        // expected-remark@above {{Successors: {3}}}

	if totalLength == 0 {
		return 0
	}
	if totalLength == -1 {
		return -1
	}
	numShards := totalLength / e.blockSize
	lastBlockSize := totalLength % e.blockSize
	lastShardSize := ceilFrac(lastBlockSize, int64(e.dataBlocks))
	return numShards*e.ShardSize() + lastShardSize
}

// ShardFileOffset - returns the effective offset where erasure reading begins.

	if totalLength == 0 {
		return 0
	}
	if totalLength == -1 {
		return -1
	}
	numShards := totalLength / e.BlockSize
	lastBlockSize := totalLength % e.BlockSize
	lastShardSize := ceilFrac(lastBlockSize, int64(e.DataBlocks))
	return numShards*e.ShardSize() + lastShardSize
}

// ShardSize - returns actual shared size from erasure blockSize.
func (e ErasureInfo) ShardSize() int64 {

                         const int64_t* const* dims, const int* num_dims,
                         int num_shapes) {
  std::vector<PartialTensorShape> shapes;
  shapes.reserve(num_shapes);
  for (int i = 0; i < num_shapes; ++i) {
    if (num_dims[i] < 0) {
      shapes.emplace_back();
    } else {
      shapes.emplace_back(ArraySlice<int64_t>(

        input_arg {
          name: "basename"
          type: DT_STRING
        }
        input_arg {
          name: "shard"
          type: DT_INT32
        }
        input_arg {
          name: "num_shards"
          type: DT_INT32
        }
        output_arg {
          name: "filename"
          type: DT_STRING
        }
      }
      op {
        name: "StatefulPartitionedCall"