parameters: (input_tensor,
                    axis=None,
                    keepdims=None,
                    name=None,
                    reduction_indices=None,
                    keep_dims=None)
return_statement: return reduce_sum(input_tensor, axis, keepdims, name)
docstring: Computes the sum of elements across dimensions of a tensor.
  
    This is the reduction operation for the elementwise `tf.math.add` op.
  
    Reduces `input_tensor` along the dimensions given in `axis`.
    Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
    of the entries in `axis`, which must be unique. If `keepdims` is true, the
    reduced dimensions are retained with length 1.
  
    If `axis` is None, all dimensions are reduced, and a
    tensor with a single element is returned.
  
    For example:
  
      >>> # x has a shape of (2, 3) (two rows and three columns):
      >>> x = tf.constant([[1, 1, 1], [1, 1, 1]])
      >>> x.numpy()
      array([[1, 1, 1],
             [1, 1, 1]], dtype=int32)
      >>> # sum all the elements
      >>> # 1 + 1 + 1 + 1 + 1+ 1 = 6
      >>> tf.reduce_sum(x).numpy()
      6
      >>> # reduce along the first dimension
      >>> # the result is [1, 1, 1] + [1, 1, 1] = [2, 2, 2]
      >>> tf.reduce_sum(x, 0).numpy()
      array([2, 2, 2], dtype=int32)
      >>> # reduce along the second dimension
      >>> # the result is [1, 1] + [1, 1] + [1, 1] = [3, 3]
      >>> tf.reduce_sum(x, 1).numpy()
      array([3, 3], dtype=int32)
      >>> # keep the original dimensions
      >>> tf.reduce_sum(x, 1, keepdims=True).numpy()
      array([[3],
             [3]], dtype=int32)
      >>> # reduce along both dimensions
      >>> # the result is 1 + 1 + 1 + 1 + 1 + 1 = 6
      >>> # or, equivalently, reduce along rows, then reduce the resultant array
      >>> # [1, 1, 1] + [1, 1, 1] = [2, 2, 2]
      >>> # 2 + 2 + 2 = 6
      >>> tf.reduce_sum(x, [0, 1]).numpy()
      6
  
    Args:
      input_tensor: The tensor to reduce. Should have numeric type.
      axis: The dimensions to reduce. If `None` (the default), reduces all
        dimensions. Must be in the range `[-rank(input_tensor),
        rank(input_tensor))`.
      keepdims: If true, retains reduced dimensions with length 1.
      name: A name for the operation (optional).
      reduction_indices: The old (deprecated) name for axis.
      keep_dims: Deprecated alias for `keepdims`.
  
    Returns:
      The reduced tensor, of the same dtype as the input_tensor.
  
    @compatibility(numpy)
    Equivalent to np.sum apart the fact that numpy upcast uint8 and int32 to
    int64 while tensorflow returns the same dtype as the input.
    @end_compatibility
docstring_summary: Computes the sum of elements across dimensions of a tensor.
docstring_tokens: Computes the sum of elements across dimensions of a tensor .
function: def reduce_sum_v1(input_tensor,
                    axis=None,
                    keepdims=None,
                    name=None,
                    reduction_indices=None,
                    keep_dims=None):
    """Computes the sum of elements across dimensions of a tensor.
  
    This is the reduction operation for the elementwise `tf.math.add` op.
  
    Reduces `input_tensor` along the dimensions given in `axis`.
    Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
    of the entries in `axis`, which must be unique. If `keepdims` is true, the
    reduced dimensions are retained with length 1.
  
    If `axis` is None, all dimensions are reduced, and a
    tensor with a single element is returned.
  
    For example:
  
      >>> # x has a shape of (2, 3) (two rows and three columns):
      >>> x = tf.constant([[1, 1, 1], [1, 1, 1]])
      >>> x.numpy()
      array([[1, 1, 1],
             [1, 1, 1]], dtype=int32)
      >>> # sum all the elements
      >>> # 1 + 1 + 1 + 1 + 1+ 1 = 6
      >>> tf.reduce_sum(x).numpy()
      6
      >>> # reduce along the first dimension
      >>> # the result is [1, 1, 1] + [1, 1, 1] = [2, 2, 2]
      >>> tf.reduce_sum(x, 0).numpy()
      array([2, 2, 2], dtype=int32)
      >>> # reduce along the second dimension
      >>> # the result is [1, 1] + [1, 1] + [1, 1] = [3, 3]
      >>> tf.reduce_sum(x, 1).numpy()
      array([3, 3], dtype=int32)
      >>> # keep the original dimensions
      >>> tf.reduce_sum(x, 1, keepdims=True).numpy()
      array([[3],
             [3]], dtype=int32)
      >>> # reduce along both dimensions
      >>> # the result is 1 + 1 + 1 + 1 + 1 + 1 = 6
      >>> # or, equivalently, reduce along rows, then reduce the resultant array
      >>> # [1, 1, 1] + [1, 1, 1] = [2, 2, 2]
      >>> # 2 + 2 + 2 = 6
      >>> tf.reduce_sum(x, [0, 1]).numpy()
      6
  
    Args:
      input_tensor: The tensor to reduce. Should have numeric type.
      axis: The dimensions to reduce. If `None` (the default), reduces all
        dimensions. Must be in the range `[-rank(input_tensor),
        rank(input_tensor))`.
      keepdims: If true, retains reduced dimensions with length 1.
      name: A name for the operation (optional).
      reduction_indices: The old (deprecated) name for axis.
      keep_dims: Deprecated alias for `keepdims`.
  
    Returns:
      The reduced tensor, of the same dtype as the input_tensor.
  
    @compatibility(numpy)
    Equivalent to np.sum apart the fact that numpy upcast uint8 and int32 to
    int64 while tensorflow returns the same dtype as the input.
    @end_compatibility
    """
    axis = deprecation.deprecated_argument_lookup("axis", axis,
                                                  "reduction_indices",
                                                  reduction_indices)
    keepdims = deprecation.deprecated_argument_lookup("keepdims", keepdims,
                                                      "keep_dims", keep_dims)
    return reduce_sum(input_tensor, axis, keepdims, name)
function_tokens: def reduce_sum_v1 ( input_tensor , axis = None , keepdims = None , name = None , reduction_indices = None , keep_dims = None ) : axis = deprecation . deprecated_argument_lookup ( "axis" , axis , "reduction_indices" , reduction_indices ) keepdims = deprecation . deprecated_argument_lookup ( "keepdims" , keepdims , "keep_dims" , keep_dims ) return reduce_sum ( input_tensor , axis , keepdims , name )