* \brief Get the data as an order 1 Eigen tensor * \details this returns the full tensor contents as a one dimensional Eigen tensor which can be used

* \brief Get the data as an order 2 tensor including batch size * \details this returns the full tensor contents as a two dimensional Eigen tensor wh

* \brief Get number of dimensions * \return Number of dimensions */

Returns the value of the expression.

* \brief Size of the first dimension * \return Size of the first dimension */

* \brief Change the number of dimensions * * \param int New number of dimensions */

* \brief Append a layer at the end of the network * \details [long description] * * \param model [description] * \param layer [description

* \brief Size of a batch (product of all dimensions) * \return Size of a batch */

Returns the specific dimension. This returns `1` if the specifie dimension does not exist. # Examples ``` # use dynet::Dim; let dim = Dim::from(([3

* \brief Batch dimension * \return Batch dimension */

* \brief Size of the second dimension (or 1 if only one dimension) * \return Size of the second dimension (or 1 if only one dimension) */

* \brief Shape of the parameter * * \return Shape as a `Dim` object */

* * \brief Reset for new sequence * \details call this before add_input and after new_graph, * when starting a new sequence on the same hyper

* \brief Get the data as an Eigen matrix * \return Eigen matrix */

Returns the total size of the dim. The returned value is equal to the product of the number of elements in a batch and the number of batches (`batch_

Runs forward pass from first node to given one.

* \brief Set the batch dimension to 1 * \return 1-batch version of this instance */

* \brief Insert a dimension to the end * \param n the size of the new dimension */

Inputs vector/matrix/tensor.

* \brief Set specific dimension * \details Set the value of a specific dimension to an arbitrary value * * \param i Dimension index * \par

Gets weight decay lambda value.