This is fpr reinforcement learning policy sampler :return:

r""" Get the Tuner class by name Args: tuner_name: the name of the Tuner Returns: the class of the Tuner

Convert half-precision to IEEE single-precision. Credit for this goes to [Jeroen van der Zijp](ftp://ftp.fox-toolkit.org/pub/fasthalffloatconversion.p

Infer the dependency of all operations with the given op as the last operation. Operator A is depending on B if A uses the output(s) of B

Init the tensor count dict

r""" all PEFT methods must implement the inject method, inject the peft method into the base model. Args: base_model: the

Returns: True if the tensor is a dummy tensor

Returns true iff this is NAN (not a number).

Returns: True if the internal data is transposed; otherwise False.

Check for infinity. See also:** Documentation for [std::isinf](https://en.cppreference.com/w/cpp/numeric/math/isinf). \param arg number to check \retv

Returns: the L1 norm.

L2 norm, Do not use Nrm2 (name conflict).

Returns: the L2 norm.

prepare train data to embedded word vector sequence given sequence limit for testing return: questions_encoded: np ndarray, shape

prepare train data to embedded word vector sequence given sequence limit return: questions_encoded: np ndarray, shape

Load the dataset and apply one-hot encoding to features (all columns or specific columns). Targets will first be one-hot encoded and then con

Read the image from a give path

Load the model states and auxiliary states from disk. Usage: m = MyModel() m.compile(...) aux_states = m.

Load the model states and auxiliary states from disk. Usage: m = MyModel() m.compile(...) aux_states = m.

Loads a vocabulary file into a dictionary.