class_docstr: Client class for invoking RPCs to the server.
class_docstr_tok: ["Client","class","for","invoking","RPCs","to","the","server","."]
docstr: Method for making RPC calls to remote server.
  
  This invokes RPC to the server, executing the registered method_name
  remotely.
  Args:
    method_name: Remote registered method to invoke
    args: List of arguments for the registered method.
    output_specs: Output specs for the output from method.
       For example, if tf.function is: @tf.function(input_signature=[
         tf.TensorSpec([], tf.int32), tf.TensorSpec([], tf.int32) ])
        def multiply_fn(a, b): return tf.math.multiply(a, b)
      output_spec is: tf.TensorSpec((), tf.int32)  If you have access to TF
        Function, the output specs can be generated
     from tf.function by calling: output_specs =
       tf.nest.map_structure(tf.type_spec_from_value,
       tf_function.get_concrete_function().structured_outputs  If output_specs
       are not provided, flattened list of tensors will be returned in
       response.
    timeout_in_ms: Timeout for this call. If 0, default client timeout will be
      used.
  
  Returns:
    An instance of `StatusOrResult` class with the following available
    methods.
      * `is_ok()`:
          Returns True of RPC was successful.
      * `get_error()`:
          Returns TF error_code and error message for the RPC.
      * `get_value()`:
          Returns the returned value from remote TF function execution
          when RPC is successful.
  
    Calling any of the above methods will block till RPC is completed and
    result is available.
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code: def call(
      self,
      method_name: str,
      args: Optional[Sequence[core_tf_types.Tensor]] = None,
      output_specs=None,
      timeout_in_ms=0,
  ):
      """Method for making RPC calls to remote server.
  
      This invokes RPC to the server, executing the registered method_name
      remotely.
      Args:
        method_name: Remote registered method to invoke
        args: List of arguments for the registered method.
        output_specs: Output specs for the output from method.
           For example, if tf.function is: @tf.function(input_signature=[
             tf.TensorSpec([], tf.int32), tf.TensorSpec([], tf.int32) ])
            def multiply_fn(a, b): return tf.math.multiply(a, b)
          output_spec is: tf.TensorSpec((), tf.int32)  If you have access to TF
            Function, the output specs can be generated
         from tf.function by calling: output_specs =
           tf.nest.map_structure(tf.type_spec_from_value,
           tf_function.get_concrete_function().structured_outputs  If output_specs
           are not provided, flattened list of tensors will be returned in
           response.
        timeout_in_ms: Timeout for this call. If 0, default client timeout will be
          used.
  
      Returns:
        An instance of `StatusOrResult` class with the following available
        methods.
          * `is_ok()`:
              Returns True of RPC was successful.
          * `get_error()`:
              Returns TF error_code and error message for the RPC.
          * `get_value()`:
              Returns the returned value from remote TF function execution
              when RPC is successful.
  
        Calling any of the above methods will block till RPC is completed and
        result is available.
      """
      raise NotImplementedError(
          "Must be implemented in inherited classes."
      )
  
imports: from typing import Optional, Sequence, Union
  import tensorflow.distribute.experimental.rpc.kernels.gen_rpc_ops
  from tensorflow.distribute.experimental.rpc.proto import tf_rpc_service_pb2
  from tensorflow.python.eager import context
  from tensorflow.python.eager import def_function
  from tensorflow.python.eager import function
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import none_tensor
  from tensorflow.python.framework import type_spec
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.saved_model import nested_structure_coder
  from tensorflow.python.types import core
  from tensorflow.python.util import nest
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 2 for calling the Client obj's underlying member method code with example usage: obj.call(method_name, args, output_specs, timeout_in_ms) without return types