Bitwise reduction (logical AND). Arguments: x: Tensor or variable. axis: axis along which to perform the reduction. keepdims: whe

Creates a `Dataset` with a single element, comprising the given tensors. Note that if `tensors` contains a NumPy array, and eager execution is no

Return the index of the source output that produces the data carried by this edge. The special value kControlSlot is used for control dependencies.

Proto-style accessors

Ensures that `tensor` is being traced by this tape. Args: tensor: a Tensor or list of Tensors. Raises: ValueError: if it encount

The device of this variable.

Construct a TensorArray for holding Tensors of type 'dtype' with 'N' elements. While the underlying storage is a std::vector and can hold more than M

Maps `map_func` across the elements of this dataset. This transformation applies `map_func` to each element of this dataset, and returns a ne

Runs a single gradient update on a single batch of data. Arguments: x: Input data. It could be: - A Numpy array (or array-like)

Sum of the values in a tensor, alongside the specified axis. Arguments: x: A tensor or variable. axis: An integer, the axis to sum over

Returns the number of operands to this instruction.

Entry point for C node plugin API to report an error.

Returns a const iterator pointing to the first wrapped tensor input.

Apply gradients to variables. This is the second part of `minimize()`. It returns an `Operation` that applies gradients. Args: gra

Returns a NumPy array specifying the maximum bounds (inclusive).

Get a mutable tensor data structure. TODO(aselle): Create a safe ArrayHandle interface to avoid exposing this read/write access to structure

idx can be negative for an offset from end of dimensions. idx must be in the range [-1 * s.rank, s.rank).

Returns the `Tensor` with the given `name`. This method may be called concurrently from multiple threads. Args: name: The name of the

Enqueues a constant op onto the computation. Args: value: value for the constant, as a np.array with an explicit dtype set to one o

Returns True iff `self` is fully defined in every dimension.

Bitwise reduction (logical OR). Arguments: x: Tensor or variable. axis: axis along which to perform the reduction. keepdims: whet

Populate the output layout unless the minor_to_major array contains all -1 value, in which case the layout is considered missing and the API returns f

Updates the stats accumulator.

Broadcast the `tensor` to destinations. Args: tensor: the tensor to broadcast. destinations: the broadcast destinations. Returns

Returns a new iterator for iterating over the range of elements in this dataset. This method may be called multiple times on the same instance, and t

Encoder using LSTM. Args: x: tensor of size [num_children, num_groups, embedding_size] Returns: last_c, last_h: tensors of size

DEPRECATED: Use outputs.

Return the list of operations in the graph. You can modify the operations in place, but modifications to the list such as inserts/delete have

Asserts that a Numpy ndarray and a TensorFlow tensor have the same shape. Args: np_array: A Numpy ndarray or Numpy scalar. tf_tensor:

Restore a reader to its initial clean state. Args: name: A name for the operation (optional). Returns: The created Operation.

Deprecated, do NOT use! This is an alias of `self.__call__`. Arguments: inputs: Input tensor(s). *args: additional positional ar

Returns a Thread wrapper that asserts 'target' completes successfully. This method should be used to create all threads in test cases, as oth

Return a slot named `name` created for `var` by the Optimizer. Some `Optimizer` subclasses use additional variables. For example `Momentum`

Serialize an accumulator for a remote call.

If this is a tuple, returns its sequence of constituent Shape objects. Returns: Tuple sub-shapes. Raises: ValueError: if this is