Vocabulary information for warm-starting.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.estimator.VocabInfo, tf.compat.v1.train.VocabInfo
See tf.estimator.WarmStartSettings for examples of using VocabInfo to warm-start.
Args: new_vocab: [Required] A path to the new vocabulary file (used with the model to be trained). new_vocab_size: [Required] An integer indicating how many entries of the new vocabulary will used in training. num_oov_buckets: [Required] An integer indicating how many OOV buckets are associated with the vocabulary. old_vocab: [Required] A path to the old vocabulary file (used with the checkpoint to be warm-started from). old_vocab_size: [Optional] An integer indicating how many entries of the old vocabulary were used in the creation of the checkpoint. If not provided, the entire old vocabulary will be used. backup_initializer: [Optional] A variable initializer used for variables corresponding to new vocabulary entries and OOV. If not provided, these entries will be zero-initialized. axis: [Optional] Denotes what axis the vocabulary corresponds to. The default, 0, corresponds to the most common use case (embeddings or linear weights for binary classification / regression). An axis of 1 could be used for warm-starting output layers with class vocabularies.
Returns: A VocabInfo which represents the vocabulary information for warm-starting.
Raises: ValueError: axis is neither 0 or 1.


Settings for warm-starting in tf.estimator.Estimators.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.estimator.WarmStartSettings
Example Use with canned tf.estimator.DNNEstimator:
where ws could be defined as:
Warm-start all weights in the model (input layer and hidden weights). Either the directory or a specific checkpoint can be provided (in the case of the former, the latest checkpoint will be used):
Warm-start only the embeddings (input layer):
Warm-start all weights but the embedding parameters corresponding to sc_vocab_file have a different vocab from the one used in the current model:
Warm-start only sc_vocab_file embeddings (and no other variables), which have a different vocab from the one used in the current model:
Warm-start all weights but the parameters corresponding to sc_vocab_file have a different vocab from the one used in current checkpoint, and only 100 of those entries were used:
Warm-start all weights but the parameters corresponding to sc_vocab_file have a different vocab from the one used in current checkpoint and the parameters corresponding to sc_vocab_list have a different name from the current checkpoint:
Warm-start all TRAINABLE variables:
Warm-start all variables (including non-TRAINABLE):
Warm-start non-TRAINABLE variables "v1", "v1/Momentum", and "v2" but not "v2/momentum":
Defaults to '.*', which warm-starts all variables in the TRAINABLE_VARIABLES collection. Note that this excludes variables such as accumulators and moving statistics from batch norm.


Checks whether the current thread has eager execution enabled.
Eager execution is enabled by default and this API returns True in most of cases. However, this API might return False in the following use cases.
Inside tf.function:
Inside tf.function after tf.config.run_functions_eagerly(True) is called:
Inside a transformation function for tf.dataset:


Returns a tensor with a length 1 axis inserted at index axis.
Given a tensor input, this operation inserts a dimension of length 1 at the dimension index axis of input's shape. The dimension index follows Python indexing rules: It's zero-based, a negative index it is counted backward from the end.
This operation is useful to:
If you have a single image of shape [height, width, channels]:
You can add an outer batch axis by passing axis=0:
The new axis location matches Python list.insert(axis, 1):
Following standard Python indexing rules, a negative axis counts from the end so axis=-1 adds an inner most dimension:
This operation requires that axis is a valid index for input.shape, following Python indexing rules:
This operation is related to:


Public API for tf.experimental namespace.
dlpack module: Public API for tf.experimental.dlpack namespace.
numpy module: # tf.experimental.numpy: NumPy API on TensorFlow.
tensorrt module: Public API for tf.experimental.tensorrt namespace.
class Optional: Represents a value that may or may not be present.
async_clear_error(...): Clear pending operations and error statuses in async execution.
async_scope(...): Context manager for grouping async operations.
function_executor_type(...): Context manager for setting the executor of eager defined functions.
register_filesystem_plugin(...): Loads a TensorFlow FileSystem plugin.


Clear pending operations and error statuses in async execution.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.experimental.async_clear_error
In async execution mode, an error in op/function execution can lead to errors in subsequent ops/functions that are scheduled but not yet executed. Calling this method clears all pending operations and reset the async execution state.


Context manager for grouping async operations.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.experimental.async_scope
Ops/function calls inside the scope can return before finishing the actual execution. When exiting the async scope, a synchronization barrier will be automatically added to ensure the completion of all async op and function execution, potentially raising exceptions if async execution results in an error state.
Users may write the following code to asynchronuously invoke train_step_fn and log the loss metric for every num_steps steps in a training loop. train_step_fn internally consumes data using iterator.get_next(), and may throw OutOfRangeError when running out of data. In the case:


Public API for tf.experimental.dlpack namespace.
from_dlpack(...): Returns the Tensorflow eager tensor.
to_dlpack(...): Returns the dlpack capsule representing the tensor.


Returns the Tensorflow eager tensor.
The returned tensor uses the memory shared by dlpack capsules from other framework.


Returns the dlpack capsule representing the tensor.
This operation ensures the underlying data memory is ready when returns.


Context manager for setting the executor of eager defined functions.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.experimental.function_executor_type
Eager defined functions are functions decorated by tf.contrib.eager.defun.


This module provides a subset of NumPy API, built on top of TensorFlow operations. APIs are based on and have been tested with NumPy 1.16 version.
The set of supported APIs may be expanded over time. Also future releases may change the baseline version of NumPy API being supported. A list of some systematic differences with NumPy are listed later in the "Differences with NumPy" section.
Please also see TensorFlow NumPy Guide.
In the code snippets below, we will assume that tf.experimental.numpy is imported as tnp and NumPy is imported as np
The module provides an ndarray class which wraps an immutable tf.Tensor. Additional functions are provided which accept array-like objects. Here array-like objects includes ndarrays as defined by this module, as well as tf.Tensor, in addition to types accepted by NumPy.
A subset of NumPy dtypes are supported. Type promotion follows NumPy semantics.
The ndarray class implements the __array__ interface. This should allow these objects to be passed into contexts that expect a NumPy or array-like object (e.g. matplotlib).
The TF-NumPy API calls can be interleaved with TensorFlow calls without incurring Tensor data copies. This is true even if the ndarray or tf.Tensor is placed on a non-CPU device.
In general, the expected behavior should be on par with that of code involving tf.Tensor and running stateless TensorFlow functions on them.
Note that the __array_priority__ is currently chosen to be lower than tf.Tensor. Hence the + operator above returns a tf.Tensor.
Additional examples of interopability include:
Given that ndarray and functions wrap TensorFlow constructs, the code will have GPU and TPU support on par with TensorFlow. Device placement can be controlled by using with tf.device scopes. Note that these devices could be local or remote.
Eager mode execution should typically match NumPy semantics of executing op-by-op. However the same code can be executed in graph mode, by putting it inside a tf.function. The function body can contain NumPy code, and the inputs can be ndarray as well.
Python control flow based on ndarray values will be translated by autograph into tf.cond and tf.while_loop constructs. The code can be XLA compiled for further optimizations.
However, note that graph mode execution can change behavior of certain operations since symbolic execution may not have information that is computed during runtime. Some differences are:
ndarrays currently wrap immutable tf.Tensor. Hence mutation operations like slice assigns are not supported. This may change in the future. Note however that one can directly construct a tf.Variable and use that with the TF-NumPy APIs.
Here is a non-exhaustive list of differences:
random module: Public API for tf.experimental.numpy.random namespace.
class bool_: Boolean type (True or False), stored as a byte.
class complex128: Complex number type composed of two double-precision floating-point
class complex64: Complex number type composed of two single-precision floating-point
class complex_: Complex number type composed of two double-precision floating-point
class float16: Half-precision floating-point number type.
class float32: Single-precision floating-point number type, compatible with C float.
class float64: Double-precision floating-point number type, compatible with Python float
class float_: Double-precision floating-point number type, compatible with Python float
class iinfo: iinfo(type)
class inexact: Abstract base class of all numeric scalar types with a (potentially)
class int16: Signed integer type, compatible with C short.
class int32: Signed integer type, compatible with C int.
class int64: Signed integer type, compatible with Python int anc C long.
class int8: Signed integer type, compatible with C char.
class int_: Signed integer type, compatible with Python int anc C long.
class ndarray: Equivalent of numpy.ndarray backed by TensorFlow tensors.
class object_: Any Python object.
class string_: bytes(iterable_of_ints) -> bytes
class uint16: Unsigned integer type, compatible with C unsigned short.
class uint32: Unsigned integer type, compatible with C unsigned int.
class uint64: Unsigned integer type, compatible with C unsigned long.
class uint8: Unsigned integer type, compatible with C unsigned char.
class unicode_: str(object='') -> str
abs(...): TensorFlow variant of NumPy's abs.
absolute(...): TensorFlow variant of NumPy's absolute.
add(...): TensorFlow variant of NumPy's add.
all(...): TensorFlow variant of NumPy's all.
allclose(...): TensorFlow variant of NumPy's allclose.
amax(...): TensorFlow variant of NumPy's amax.
amin(...): TensorFlow variant of NumPy's amin.
angle(...): TensorFlow variant of NumPy's angle.
any(...): TensorFlow variant of NumPy's any.
append(...): TensorFlow variant of NumPy's append.
arange(...): TensorFlow variant of NumPy's arange.
arccos(...): TensorFlow variant of NumPy's arccos.
arccosh(...): TensorFlow variant of NumPy's arccosh.
arcsin(...): TensorFlow variant of NumPy's arcsin.
arcsinh(...): TensorFlow variant of NumPy's arcsinh.
arctan(...): TensorFlow variant of NumPy's arctan.
arctan2(...): TensorFlow variant of NumPy's arctan2.
arctanh(...): TensorFlow variant of NumPy's arctanh.
argmax(...): TensorFlow variant of NumPy's argmax.
argmin(...): TensorFlow variant of NumPy's argmin.
argsort(...): TensorFlow variant of NumPy's argsort.
around(...): TensorFlow variant of NumPy's around.
array(...): TensorFlow variant of NumPy's array.
array_equal(...): TensorFlow variant of NumPy's array_equal.
asanyarray(...): TensorFlow variant of NumPy's asanyarray.
asarray(...): TensorFlow variant of NumPy's asarray.
ascontiguousarray(...): TensorFlow variant of NumPy's ascontiguousarray.
atleast_1d(...): TensorFlow variant of NumPy's atleast_1d.
atleast_2d(...): TensorFlow variant of NumPy's atleast_2d.
atleast_3d(...): TensorFlow variant of NumPy's atleast_3d.
average(...): TensorFlow variant of NumPy's average.
bitwise_and(...): TensorFlow variant of NumPy's bitwise_and.
bitwise_not(...): TensorFlow variant of NumPy's bitwise_not.
bitwise_or(...): TensorFlow variant of NumPy's bitwise_or.
bitwise_xor(...): TensorFlow variant of NumPy's bitwise_xor.
broadcast_arrays(...): TensorFlow variant of NumPy's broadcast_arrays.
broadcast_to(...): TensorFlow variant of NumPy's broadcast_to.
cbrt(...): TensorFlow variant of NumPy's cbrt.
ceil(...): TensorFlow variant of NumPy's ceil.
clip(...): TensorFlow variant of NumPy's clip.
compress(...): TensorFlow variant of NumPy's compress.
concatenate(...): TensorFlow variant of NumPy's concatenate.
conj(...): TensorFlow variant of NumPy's conj.
conjugate(...): TensorFlow variant of NumPy's conjugate.
copy(...): TensorFlow variant of NumPy's copy.
cos(...): TensorFlow variant of NumPy's cos.
cosh(...): TensorFlow variant of NumPy's cosh.
count_nonzero(...): TensorFlow variant of NumPy's count_nonzero.
cross(...): TensorFlow variant of NumPy's cross.
cumprod(...): TensorFlow variant of NumPy's cumprod.
cumsum(...): TensorFlow variant of NumPy's cumsum.
deg2rad(...): TensorFlow variant of NumPy's deg2rad.
diag(...): TensorFlow variant of NumPy's diag.
diag_indices(...): TensorFlow variant of NumPy's diag_indices.
diagflat(...): TensorFlow variant of NumPy's diagflat.
diagonal(...): TensorFlow variant of NumPy's diagonal.
diff(...): TensorFlow variant of NumPy's diff.
divide(...): TensorFlow variant of NumPy's divide.
divmod(...): TensorFlow variant of NumPy's divmod.
dot(...): TensorFlow variant of NumPy's dot.
dsplit(...): TensorFlow variant of NumPy's dsplit.
dstack(...): TensorFlow variant of NumPy's dstack.
einsum(...): TensorFlow variant of NumPy's einsum.
empty(...): TensorFlow variant of NumPy's empty.
empty_like(...): TensorFlow variant of NumPy's empty_like.
equal(...): TensorFlow variant of NumPy's equal.
exp(...): TensorFlow variant of NumPy's exp.
exp2(...): TensorFlow variant of NumPy's exp2.
expand_dims(...): TensorFlow variant of NumPy's expand_dims.
expm1(...): TensorFlow variant of NumPy's expm1.
eye(...): TensorFlow variant of NumPy's eye.
fabs(...): TensorFlow variant of NumPy's fabs.
finfo(...): TensorFlow variant of NumPy's finfo.
fix(...): TensorFlow variant of NumPy's fix.
flip(...): TensorFlow variant of NumPy's flip.
fliplr(...): TensorFlow variant of NumPy's fliplr.
flipud(...): TensorFlow variant of NumPy's flipud.
float_power(...): TensorFlow variant of NumPy's float_power.
floor(...): TensorFlow variant of NumPy's floor.
floor_divide(...): TensorFlow variant of NumPy's floor_divide.
full(...): TensorFlow variant of NumPy's full.
full_like(...): TensorFlow variant of NumPy's full_like.
gcd(...): TensorFlow variant of NumPy's gcd.
geomspace(...): TensorFlow variant of NumPy's geomspace.
greater(...): TensorFlow variant of NumPy's greater.
greater_equal(...): TensorFlow variant of NumPy's greater_equal.
heaviside(...): TensorFlow variant of NumPy's heaviside.
hsplit(...): TensorFlow variant of NumPy's hsplit.
hstack(...): TensorFlow variant of NumPy's hstack.
hypot(...): TensorFlow variant of NumPy's hypot.
identity(...): TensorFlow variant of NumPy's identity.
imag(...): TensorFlow variant of NumPy's imag.
inner(...): TensorFlow variant of NumPy's inner.
isclose(...): TensorFlow variant of NumPy's isclose.
iscomplex(...): TensorFlow variant of NumPy's iscomplex.
iscomplexobj(...): TensorFlow variant of NumPy's iscomplexobj.
isfinite(...): TensorFlow variant of NumPy's isfinite.
isinf(...): TensorFlow variant of NumPy's isinf.
isnan(...): TensorFlow variant of NumPy's isnan.
isneginf(...): TensorFlow variant of NumPy's isneginf.
isposinf(...): TensorFlow variant of NumPy's isposinf.
isreal(...): TensorFlow variant of NumPy's isreal.
isrealobj(...): TensorFlow variant of NumPy's isrealobj.
isscalar(...): TensorFlow variant of NumPy's isscalar.
issubdtype(...): Returns True if first argument is a typecode lower/equal in type hierarchy.
ix_(...): TensorFlow variant of NumPy's ix_.
kron(...): TensorFlow variant of NumPy's kron.
lcm(...): TensorFlow variant of NumPy's lcm.
less(...): TensorFlow variant of NumPy's less.
less_equal(...): TensorFlow variant of NumPy's less_equal.
linspace(...): TensorFlow variant of NumPy's linspace.
log(...): TensorFlow variant of NumPy's log.
log10(...): TensorFlow variant of NumPy's log10.
log1p(...): TensorFlow variant of NumPy's log1p.
log2(...): TensorFlow variant of NumPy's log2.
logaddexp(...): TensorFlow variant of NumPy's logaddexp.
logaddexp2(...): TensorFlow variant of NumPy's logaddexp2.
logical_and(...): TensorFlow variant of NumPy's logical_and.
logical_not(...): TensorFlow variant of NumPy's logical_not.
logical_or(...): TensorFlow variant of NumPy's logical_or.
logical_xor(...): TensorFlow variant of NumPy's logical_xor.
logspace(...): TensorFlow variant of NumPy's logspace.
matmul(...): TensorFlow variant of NumPy's matmul.
max(...): TensorFlow variant of NumPy's max.
maximum(...): TensorFlow variant of NumPy's maximum.
mean(...): TensorFlow variant of NumPy's mean.
meshgrid(...): TensorFlow variant of NumPy's meshgrid.
min(...): TensorFlow variant of NumPy's min.
minimum(...): TensorFlow variant of NumPy's minimum.
mod(...): TensorFlow variant of NumPy's mod.
moveaxis(...): TensorFlow variant of NumPy's moveaxis.
multiply(...): TensorFlow variant of NumPy's multiply.
nanmean(...): TensorFlow variant of NumPy's nanmean.
nanprod(...): TensorFlow variant of NumPy's nanprod.
nansum(...): TensorFlow variant of NumPy's nansum.
ndim(...): TensorFlow variant of NumPy's ndim.
negative(...): TensorFlow variant of NumPy's negative.
nextafter(...): TensorFlow variant of NumPy's nextafter.
nonzero(...): TensorFlow variant of NumPy's nonzero.
not_equal(...): TensorFlow variant of NumPy's not_equal.
ones(...): TensorFlow variant of NumPy's ones.
ones_like(...): TensorFlow variant of NumPy's ones_like.
outer(...): TensorFlow variant of NumPy's outer.
pad(...): TensorFlow variant of NumPy's pad.
polyval(...): TensorFlow variant of NumPy's polyval.
positive(...): TensorFlow variant of NumPy's positive.
power(...): TensorFlow variant of NumPy's power.
prod(...): TensorFlow variant of NumPy's prod.
promote_types(...): TensorFlow variant of NumPy's promote_types.
ptp(...): TensorFlow variant of NumPy's ptp.
rad2deg(...): TensorFlow variant of NumPy's rad2deg.
ravel(...): TensorFlow variant of NumPy's ravel.
real(...): TensorFlow variant of NumPy's real.
reciprocal(...): TensorFlow variant of NumPy's reciprocal.
remainder(...): TensorFlow variant of NumPy's remainder.
repeat(...): TensorFlow variant of NumPy's repeat.
reshape(...): TensorFlow variant of NumPy's reshape.
result_type(...): TensorFlow variant of NumPy's result_type.
roll(...): TensorFlow variant of NumPy's roll.
rot90(...): TensorFlow variant of NumPy's rot90.
round(...): TensorFlow variant of NumPy's round.
select(...): TensorFlow variant of NumPy's select.
shape(...): TensorFlow variant of NumPy's shape.
sign(...): TensorFlow variant of NumPy's sign.
signbit(...): TensorFlow variant of NumPy's signbit.
sin(...): TensorFlow variant of NumPy's sin.
sinc(...): TensorFlow variant of NumPy's sinc.
sinh(...): TensorFlow variant of NumPy's sinh.
size(...): TensorFlow variant of NumPy's size.
sort(...): TensorFlow variant of NumPy's sort.
split(...): TensorFlow variant of NumPy's split.
sqrt(...): TensorFlow variant of NumPy's sqrt.
square(...): TensorFlow variant of NumPy's square.
squeeze(...): TensorFlow variant of NumPy's squeeze.
stack(...): TensorFlow variant of NumPy's stack.
std(...): TensorFlow variant of NumPy's std.
subtract(...): TensorFlow variant of NumPy's subtract.
sum(...): TensorFlow variant of NumPy's sum.
swapaxes(...): TensorFlow variant of NumPy's swapaxes.
take(...): TensorFlow variant of NumPy's take.
take_along_axis(...): TensorFlow variant of NumPy's take_along_axis.
tan(...): TensorFlow variant of NumPy's tan.
tanh(...): TensorFlow variant of NumPy's tanh.
tensordot(...): TensorFlow variant of NumPy's tensordot.
tile(...): TensorFlow variant of NumPy's tile.
trace(...): TensorFlow variant of NumPy's trace.
transpose(...): TensorFlow variant of NumPy's transpose.
tri(...): TensorFlow variant of NumPy's tri.
tril(...): TensorFlow variant of NumPy's tril.
triu(...): TensorFlow variant of NumPy's triu.
true_divide(...): TensorFlow variant of NumPy's true_divide.
vander(...): TensorFlow variant of NumPy's vander.
var(...): TensorFlow variant of NumPy's var.
vdot(...): TensorFlow variant of NumPy's vdot.
vsplit(...): TensorFlow variant of NumPy's vsplit.
vstack(...): TensorFlow variant of NumPy's vstack.
where(...): TensorFlow variant of NumPy's where.
zeros(...): TensorFlow variant of NumPy's zeros.
zeros_like(...): TensorFlow variant of NumPy's zeros_like.


TensorFlow variant of NumPy's abs.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.abs.


TensorFlow variant of NumPy's absolute.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.absolute.


TensorFlow variant of NumPy's add.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.add.


TensorFlow variant of NumPy's all.
Unsupported arguments: out.
See the NumPy documentation for numpy.all.


TensorFlow variant of NumPy's allclose.
See the NumPy documentation for numpy.allclose.


TensorFlow variant of NumPy's amax.
Unsupported arguments: out, initial, where.
See the NumPy documentation for numpy.amax.


TensorFlow variant of NumPy's amin.
Unsupported arguments: out, initial, where.
See the NumPy documentation for numpy.amin.


TensorFlow variant of NumPy's angle.
See the NumPy documentation for numpy.angle.


TensorFlow variant of NumPy's any.
Unsupported arguments: out.
See the NumPy documentation for numpy.any.


TensorFlow variant of NumPy's append.
See the NumPy documentation for numpy.append.


TensorFlow variant of NumPy's arange.
Returns step-separated values in the range [start, stop).
Args: start: Start of the interval. Included in the range. stop: End of the interval. If not specified, start is treated as 0 and start value is used as stop. If specified, it is not included in the range if step is integer. When step is floating point, it may or may not be included. step: The difference between 2 consecutive values in the output range. It is recommended to use linspace instead of using non-integer values for step. dtype: Optional. Type of the resulting ndarray. Could be a python type, a NumPy type or a TensorFlow DType. If not provided, the largest type of start, stop, step is used.
Raises: ValueError: If step is zero.
See the NumPy documentation for numpy.arange.


TensorFlow variant of NumPy's arccos.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.arccos.


TensorFlow variant of NumPy's arccosh.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.arccosh.


TensorFlow variant of NumPy's arcsin.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.arcsin.


TensorFlow variant of NumPy's arcsinh.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.arcsinh.


TensorFlow variant of NumPy's arctan.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.arctan.


TensorFlow variant of NumPy's arctan2.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.arctan2.


TensorFlow variant of NumPy's arctanh.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.arctanh.


TensorFlow variant of NumPy's argmax.
Unsupported arguments: out.
See the NumPy documentation for numpy.argmax.


TensorFlow variant of NumPy's argmin.
Unsupported arguments: out.
See the NumPy documentation for numpy.argmin.


TensorFlow variant of NumPy's argsort.
See the NumPy documentation for numpy.argsort.


TensorFlow variant of NumPy's around.
Unsupported arguments: out.
See the NumPy documentation for numpy.around.


TensorFlow variant of NumPy's array.
Since Tensors are immutable, a copy is made only if val is placed on a
different device than the current one. Even if copy is False, a new Tensor may need to be built to satisfy dtype and ndim. This is used only if val is an ndarray or a Tensor.
See the NumPy documentation for numpy.array.


TensorFlow variant of NumPy's array_equal.
Unsupported arguments: equal_nan.
See the NumPy documentation for numpy.array_equal.


TensorFlow variant of NumPy's asanyarray.
Unsupported arguments: order.
See the NumPy documentation for numpy.asanyarray.


TensorFlow variant of NumPy's asarray.
Unsupported arguments: order.
See the NumPy documentation for numpy.asarray.


TensorFlow variant of NumPy's ascontiguousarray.
See the NumPy documentation for numpy.ascontiguousarray.


TensorFlow variant of NumPy's atleast_1d.
See the NumPy documentation for numpy.atleast_1d.


TensorFlow variant of NumPy's atleast_2d.
See the NumPy documentation for numpy.atleast_2d.


TensorFlow variant of NumPy's atleast_3d.
See the NumPy documentation for numpy.atleast_3d.


TensorFlow variant of NumPy's average.
See the NumPy documentation for numpy.average.


TensorFlow variant of NumPy's bitwise_and.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.bitwise_and.


TensorFlow variant of NumPy's bitwise_not.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.bitwise_not.


TensorFlow variant of NumPy's bitwise_or.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.bitwise_or.


TensorFlow variant of NumPy's bitwise_xor.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.bitwise_xor.


Boolean type (True or False), stored as a byte.
Character code: '?'. Alias: np.bool8.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
newbyteorder(new_order='S')
Return a new dtype with a different byte order.
Changes are also made in all fields and sub-arrays of the data type.
The new_order code can be any from the following:
new_order : str, optional Byte order to force; a value from the byte order specifications above. The default value ('S') results in swapping the current byte order. The code does a case-insensitive check on the first letter of new_order for the alternatives above. For example, any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
new_dtype : dtype New dtype object with the given change to the byte order.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
abs(self)
Return self+value.
Return self&value.
self != 0
Return self==value.
Return self//value.
Return self>=value.
Return self[key].
Return self>value.
~self
Return self<=value.
Return self<value.
Return self%value.
Return self*value.
Return self!=value.
-self
Return self|value.
+self
Return pow(self, value, mod).
Return value+self.
Return value&self.
Return value//self.
Return value%self.
Return value*self.
Return value|self.
Return pow(value, self, mod).
Return value-self.
Return value/self.
Return value^self.
Return self-value.
Return self/value.
Return self^value.


TensorFlow variant of NumPy's broadcast_arrays.
Unsupported arguments: subok.
See the NumPy documentation for numpy.broadcast_arrays.


TensorFlow variant of NumPy's broadcast_to.
Unsupported arguments: subok.
See the NumPy documentation for numpy.broadcast_to.


TensorFlow variant of NumPy's cbrt.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.cbrt.


TensorFlow variant of NumPy's ceil.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.ceil.


TensorFlow variant of NumPy's clip.
Main aliases
tf.experimental.numpy.ndarray.clip
Unsupported arguments: out, kwargs.
See the NumPy documentation for numpy.clip.


Complex number type composed of two double-precision floating-point
Inherits From: inexact
Main aliases
tf.experimental.numpy.complex_
numbers, compatible with Python complex. Character code: 'D'. Canonical name: np.cdouble. Alias: np.cfloat. Alias: np.complex_. Alias on this platform: np.complex128: Complex number type composed of 2 64-bit-precision floating-point numbers.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
newbyteorder(new_order='S')
Return a new dtype with a different byte order.
Changes are also made in all fields and sub-arrays of the data type.
The new_order code can be any from the following:
new_order : str, optional Byte order to force; a value from the byte order specifications above. The default value ('S') results in swapping the current byte order. The code does a case-insensitive check on the first letter of new_order for the alternatives above. For example, any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
new_dtype : dtype New dtype object with the given change to the byte order.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
abs(self)
Return self+value.
Return self&value.
self != 0
Return self==value.
Return self//value.
Return self>=value.
Return self[key].
Return self>value.
~self
Return self<=value.
Return self<value.
Return self%value.
Return self*value.
Return self!=value.
-self
Return self|value.
+self
Return pow(self, value, mod).
Return value+self.
Return value&self.
Return value//self.
Return value%self.
Return value*self.
Return value|self.
Return pow(value, self, mod).
Return value-self.
Return value/self.
Return value^self.
Return self-value.
Return self/value.
Return self^value.


Complex number type composed of two single-precision floating-point
Inherits From: inexact
numbers. Character code: 'F'. Canonical name: np.csingle. Alias: np.singlecomplex. Alias on this platform: np.complex64: Complex number type composed of 2 32-bit-precision floating-point numbers.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
newbyteorder(new_order='S')
Return a new dtype with a different byte order.
Changes are also made in all fields and sub-arrays of the data type.
The new_order code can be any from the following:
new_order : str, optional Byte order to force; a value from the byte order specifications above. The default value ('S') results in swapping the current byte order. The code does a case-insensitive check on the first letter of new_order for the alternatives above. For example, any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
new_dtype : dtype New dtype object with the given change to the byte order.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
abs(self)
Return self+value.
Return self&value.
self != 0
Return self==value.
Return self//value.
Return self>=value.
Return self[key].
Return self>value.
~self
Return self<=value.
Return self<value.
Return self%value.
Return self*value.
Return self!=value.
-self
Return self|value.
+self
Return pow(self, value, mod).
Return value+self.
Return value&self.
Return value//self.
Return value%self.
Return value*self.
Return value|self.
Return pow(value, self, mod).
Return value-self.
Return value/self.
Return value^self.
Return self-value.
Return self/value.
Return self^value.


TensorFlow variant of NumPy's compress.
Unsupported arguments: out.
See the NumPy documentation for numpy.compress.


TensorFlow variant of NumPy's concatenate.
See the NumPy documentation for numpy.concatenate.


TensorFlow variant of NumPy's conj.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.conj.


TensorFlow variant of NumPy's conjugate.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.conjugate.


TensorFlow variant of NumPy's copy.
Unsupported arguments: order, subok.
See the NumPy documentation for numpy.copy.


TensorFlow variant of NumPy's cos.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.cos.


TensorFlow variant of NumPy's cosh.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.cosh.


TensorFlow variant of NumPy's count_nonzero.
Unsupported arguments: keepdims.
See the NumPy documentation for numpy.count_nonzero.


TensorFlow variant of NumPy's cross.
See the NumPy documentation for numpy.cross.


TensorFlow variant of NumPy's cumprod.
Unsupported arguments: out.
See the NumPy documentation for numpy.cumprod.


TensorFlow variant of NumPy's cumsum.
Unsupported arguments: out.
See the NumPy documentation for numpy.cumsum.


TensorFlow variant of NumPy's deg2rad.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.deg2rad.


TensorFlow variant of NumPy's diag.
Raises an error if input is not 1- or 2-d.
See the NumPy documentation for numpy.diag.


TensorFlow variant of NumPy's diagflat.
See the NumPy documentation for numpy.diagflat.


TensorFlow variant of NumPy's diagonal.
See the NumPy documentation for numpy.diagonal.


TensorFlow variant of NumPy's diag_indices.
See the NumPy documentation for numpy.diag_indices.


TensorFlow variant of NumPy's diff.
Unsupported arguments: prepend, append.
See the NumPy documentation for numpy.diff.


TensorFlow variant of NumPy's divide.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.divide.


TensorFlow variant of NumPy's divmod.
Unsupported arguments: out1, out2, out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.divmod.


TensorFlow variant of NumPy's dot.
See the NumPy documentation for numpy.dot.


TensorFlow variant of NumPy's dsplit.
See the NumPy documentation for numpy.dsplit.


TensorFlow variant of NumPy's dstack.
See the NumPy documentation for numpy.dstack.


TensorFlow variant of NumPy's einsum.
See the NumPy documentation for numpy.einsum.


TensorFlow variant of NumPy's empty.
See the NumPy documentation for numpy.empty.


TensorFlow variant of NumPy's empty_like.
See the NumPy documentation for numpy.empty_like.


TensorFlow variant of NumPy's equal.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.equal.


TensorFlow variant of NumPy's exp.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.exp.


TensorFlow variant of NumPy's exp2.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.exp2.


TensorFlow variant of NumPy's expand_dims.
See the NumPy documentation for numpy.expand_dims.


TensorFlow variant of NumPy's expm1.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.expm1.


TensorFlow variant of NumPy's eye.
Unsupported arguments: order.
See the NumPy documentation for numpy.eye.


TensorFlow variant of NumPy's fabs.
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj.
See the NumPy documentation for numpy.fabs.


TensorFlow variant of NumPy's finfo.
Note that currently it just forwards to the numpy namesake, while tensorflow and numpy dtypes may have different properties.
See the NumPy documentation for numpy.finfo.


TensorFlow variant of NumPy's fix.
Unsupported arguments: out.
See the NumPy documentation for numpy.fix.


TensorFlow variant of NumPy's flip.
See the NumPy documentation for numpy.flip.


TensorFlow variant of NumPy's fliplr.
See the NumPy documentation for numpy.fliplr.


TensorFlow variant of NumPy's flipud.
See the NumPy documentation for numpy.flipud.


Half-precision floating-point number type.
Inherits From: inexact
Character code: 'e'. Canonical name: np.half. Alias on this platform: np.float16: 16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
half.as_integer_ratio() -> (int, int)
Return a pair of integers, whose ratio is exactly equal to the original floating point number, and with a positive denominator. Raise OverflowError on infinities and a ValueError on NaNs.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
newbyteorder(new_order='S')
Return a new dtype with a different byte order.
Changes are also made in all fields and sub-arrays of the data type.
The new_order code can be any from the following:
new_order : str, optional Byte order to force; a value from the byte order specifications above. The default value ('S') results in swapping the current byte order. The code does a case-insensitive check on the first letter of new_order for the alternatives above. For example, any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
new_dtype : dtype New dtype object with the given change to the byte order.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API.
See also the corresponding attribute of the derived class of interest.
abs(self)
Return self+value.
Return self&value.
self != 0
Return self==value.
Return self//value.
Return self>=value.
Return self[key].
Return self>value.
~self
Return self<=value.
Return self<value.
Return self%value.
Return self*value.
Return self!=value.
-self
Return self|value.
+self
Return pow(self, value, mod).
Return value+self.
Return value&self.
Return value//self.
Return value%self.
Return value*self.
Return value|self.
Return pow(value, self, mod).
Return value-self.
Return value/self.
Return value^self.
Return self-value.
Return self/value.
Return self^value.