parameters: (computation,
                     inputs=None,
                     num_shards=1,
                     infeed_queue=None,
                     device_assignment=None,
                     name=None)
return_statement: return shard(
        computation,
        inputs,
        num_shards=num_shards,
        infeed_queue=infeed_queue,
        device_assignment=device_assignment,
        name=name)
docstring: Shards `computation` along the batch dimension for parallel execution.
  
    Convenience wrapper around shard().
  
    `inputs` must be a list of Tensors or None (equivalent to an empty list).
    Each input is split into `num_shards` pieces along the 0-th dimension, and
    computation is applied to each shard in parallel.
  
    Tensors are broadcast to all shards if they are lexically captured by
    `computation`. e.g.,
  
    x = tf.constant(7)
    def computation():
      return x + 3
    ... = shard(computation, ...)
  
    The outputs from all shards are concatenated back together along their 0-th
    dimension.
  
    Inputs and outputs of the computation must be at least rank-1 Tensors.
  
    Args:
      computation: A Python function that builds a computation to apply to each
        shard of the input.
      inputs: A list of input tensors or None (equivalent to an empty list). The
        0-th dimension of each Tensor must have size divisible by `num_shards`.
      num_shards: The number of shards.
      infeed_queue: If not `None`, the `InfeedQueue` from which to append a tuple
        of arguments as inputs to `computation`.
      device_assignment: If not `None`, a `DeviceAssignment` describing the
        mapping between logical cores in the computation with physical cores in
        the TPU topology. Uses a default device assignment if `None`. The
        `DeviceAssignment` may be omitted if each shard of the computation uses
        only one core, and there is either only one shard, or the number of shards
        is equal to the number of cores in the TPU system.
      name: (Deprecated) Does nothing.
    Returns:
      A list of output tensors.
    Raises:
      ValueError: If `num_shards <= 0`
docstring_summary: Shards `computation` along the batch dimension for parallel execution.
docstring_tokens: Shards computation along the batch dimension for parallel execution .
function: def batch_parallel(computation,
                     inputs=None,
                     num_shards=1,
                     infeed_queue=None,
                     device_assignment=None,
                     name=None):
    """Shards `computation` along the batch dimension for parallel execution.
  
    Convenience wrapper around shard().
  
    `inputs` must be a list of Tensors or None (equivalent to an empty list).
    Each input is split into `num_shards` pieces along the 0-th dimension, and
    computation is applied to each shard in parallel.
  
    Tensors are broadcast to all shards if they are lexically captured by
    `computation`. e.g.,
  
    x = tf.constant(7)
    def computation():
      return x + 3
    ... = shard(computation, ...)
  
    The outputs from all shards are concatenated back together along their 0-th
    dimension.
  
    Inputs and outputs of the computation must be at least rank-1 Tensors.
  
    Args:
      computation: A Python function that builds a computation to apply to each
        shard of the input.
      inputs: A list of input tensors or None (equivalent to an empty list). The
        0-th dimension of each Tensor must have size divisible by `num_shards`.
      num_shards: The number of shards.
      infeed_queue: If not `None`, the `InfeedQueue` from which to append a tuple
        of arguments as inputs to `computation`.
      device_assignment: If not `None`, a `DeviceAssignment` describing the
        mapping between logical cores in the computation with physical cores in
        the TPU topology. Uses a default device assignment if `None`. The
        `DeviceAssignment` may be omitted if each shard of the computation uses
        only one core, and there is either only one shard, or the number of shards
        is equal to the number of cores in the TPU system.
      name: (Deprecated) Does nothing.
    Returns:
      A list of output tensors.
    Raises:
      ValueError: If `num_shards <= 0`
    """
    return shard(
        computation,
        inputs,
        num_shards=num_shards,
        infeed_queue=infeed_queue,
        device_assignment=device_assignment,
        name=name)
function_tokens: def batch_parallel ( computation , inputs = None , num_shards = 1 , infeed_queue = None , device_assignment = None , name = None ) : return shard ( computation , inputs , num_shards = num_shards , infeed_queue = infeed_queue , device_assignment = device_assignment , name = name )