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Backend Abstraction Layer for Meridian.
Modules
config module: Backend configuration for Meridian.
Classes
class ExtensionType: Base class for TensorFlow ExtensionType classes.
class RNGHandler: TensorFlow implementation.
class Tensor: A tf.Tensor represents a multidimensional array of elements.
class TensorShape: Represents the shape of a Tensor.
Functions
absolute(...): Computes the absolute value of a tensor.
allclose(...): TensorFlow variant of NumPy's allclose.
arange(...): TensorFlow implementation for arange.
argmax(...): TensorFlow implementation for argmax.
boolean_mask(...): TensorFlow implementation for boolean_mask.
broadcast_dynamic_shape(...): Computes the shape of a broadcast given symbolic shapes.
broadcast_to(...): Broadcast an array for a compatible shape.
cast(...): Casts a tensor to a new type.
concatenate(...): Concatenates tensors along one dimension.
cumsum(...): Compute the cumulative sum of the tensor x along axis.
divide(...): Computes Python style division of x by y.
divide_no_nan(...): Computes a safe divide which returns 0 if y (denominator) is zero.
einsum(...): Tensor contraction over specified indices and outer product.
equal(...): Returns the truth value of (x == y) element-wise.
exp(...): Computes exponential of x element-wise. \(y = e^x\).
expand_dims(...): Returns a tensor with a length 1 axis inserted at index axis.
fill(...): TensorFlow implementation for fill.
function(...): A wrapper for tf.function that ignores JAX-specific arguments.
gather(...): TensorFlow implementation for gather.
get_indices_where(...): TensorFlow implementation for get_indices_where.
is_nan(...): Returns which elements of x are NaN.
log(...): Computes natural logarithm of x element-wise.
make_ndarray(...): Create a numpy ndarray from a tensor.
make_tensor_proto(...): Create a TensorProto.
nanmedian(...): TensorFlow implementation for nanmedian using numpy_function.
numpy_function(...): Wraps a python function and uses it as a TensorFlow op.
ones(...): Creates a tensor with all elements set to one (1).
ones_like(...): Creates a tensor of all ones that has the same shape as the input.
rank(...): Returns the rank of a tensor.
reduce_any(...): Computes tf.math.logical_or of elements across dimensions of a tensor.
reduce_max(...): Computes tf.math.maximum of elements across dimensions of a tensor.
reduce_mean(...): Computes the mean of elements across dimensions of a tensor.
reduce_min(...): Computes the tf.math.minimum of elements across dimensions of a tensor.
reduce_std(...): Computes the standard deviation of elements across dimensions of a tensor.
reduce_sum(...): Computes the sum of elements across dimensions of a tensor.
repeat(...): Repeat elements of input.
reshape(...): Reshapes a tensor.
result_type(...): Infers the result dtype from a list of input types, backend-agnostically.
set_random_seed(...): Sets all random seeds (Python, NumPy, and backend framework, e.g. TF).
split(...): Splits a tensor value into a list of sub tensors.
stack(...): Stacks a list of rank-R tensors into one rank-(R+1) tensor.
standardize_dtype(...): Converts a backend-specific dtype to a standard string representation.
tile(...): Constructs a tensor by tiling a given tensor.
to_tensor(...): Converts input data to the currently active backend tensor type.
transpose(...): Transposes a, where a is a Tensor.
unique_with_counts(...): TensorFlow implementation for unique_with_counts.
where(...): Returns the indices of non-zero elements, or multiplexes x and y.
zeros(...): Creates a tensor with all elements set to zero.
zeros_like(...): Creates a tensor with all elements set to zero.