neulab/docprompting-conala huggingface dataset:
tf.experimental.numpy.moveaxis TensorFlow variant of NumPy's moveaxis. 
tf.experimental.numpy.moveaxis(
    a, source, destination
)
 Raises ValueError if source, destination not in (-ndim(a), ndim(a)). See the NumPy documentation for numpy.moveaxis.
tf.experimental.numpy.multiply TensorFlow variant of NumPy's multiply. 
tf.experimental.numpy.multiply(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.multiply.
tf.experimental.numpy.nanmean TensorFlow variant of NumPy's nanmean. 
tf.experimental.numpy.nanmean(
    a, axis=None, dtype=None, keepdims=None
)
 Unsupported arguments: out. See the NumPy documentation for numpy.nanmean.
tf.experimental.numpy.nanprod TensorFlow variant of NumPy's nanprod. 
tf.experimental.numpy.nanprod(
    a, axis=None, dtype=None, keepdims=False
)
 Unsupported arguments: out. See the NumPy documentation for numpy.nanprod.
tf.experimental.numpy.nansum TensorFlow variant of NumPy's nansum. 
tf.experimental.numpy.nansum(
    a, axis=None, dtype=None, keepdims=False
)
 Unsupported arguments: out. See the NumPy documentation for numpy.nansum.
tf.experimental.numpy.ndim TensorFlow variant of NumPy's ndim. 
tf.experimental.numpy.ndim(
    a
)
tf.experimental.numpy.negative TensorFlow variant of NumPy's negative. 
tf.experimental.numpy.negative(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.negative.
tf.experimental.numpy.nextafter TensorFlow variant of NumPy's nextafter. 
tf.experimental.numpy.nextafter(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.nextafter.
tf.experimental.numpy.nonzero TensorFlow variant of NumPy's nonzero. 
tf.experimental.numpy.nonzero(
    a
)
 See the NumPy documentation for numpy.nonzero.
tf.experimental.numpy.not_equal TensorFlow variant of NumPy's not_equal. 
tf.experimental.numpy.not_equal(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.not_equal.
tf.experimental.numpy.ones TensorFlow variant of NumPy's ones. 
tf.experimental.numpy.ones(
    shape, dtype=float
)
 Unsupported arguments: order. See the NumPy documentation for numpy.ones.
tf.experimental.numpy.ones_like TensorFlow variant of NumPy's ones_like. 
tf.experimental.numpy.ones_like(
    a, dtype=None
)
 Unsupported arguments: order, subok, shape. See the NumPy documentation for numpy.ones_like.
tf.experimental.numpy.outer TensorFlow variant of NumPy's outer. 
tf.experimental.numpy.outer(
    a, b
)
 Unsupported arguments: out. See the NumPy documentation for numpy.outer.
tf.experimental.numpy.pad TensorFlow variant of NumPy's pad. 
tf.experimental.numpy.pad(
    array, pad_width, mode, **kwargs
)
 Only supports modes 'constant', 'reflect' and 'symmetric' currently. See the NumPy documentation for numpy.pad.
tf.experimental.numpy.polyval TensorFlow variant of NumPy's polyval. 
tf.experimental.numpy.polyval(
    p, x
)
 See the NumPy documentation for numpy.polyval.
tf.experimental.numpy.positive TensorFlow variant of NumPy's positive. 
tf.experimental.numpy.positive(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.positive.
tf.experimental.numpy.power TensorFlow variant of NumPy's power. 
tf.experimental.numpy.power(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.power.
tf.experimental.numpy.prod TensorFlow variant of NumPy's prod. 
tf.experimental.numpy.prod(
    a, axis=None, dtype=None, keepdims=None
)
 Unsupported arguments: out, initial, where. See the NumPy documentation for numpy.prod.
tf.experimental.numpy.promote_types TensorFlow variant of NumPy's promote_types. 
tf.experimental.numpy.promote_types(
    type1, type2
)
 See the NumPy documentation for numpy.promote_types.
tf.experimental.numpy.ptp TensorFlow variant of NumPy's ptp. 
tf.experimental.numpy.ptp(
    a, axis=None, keepdims=None
)
 Unsupported arguments: out. See the NumPy documentation for numpy.ptp.
tf.experimental.numpy.rad2deg TensorFlow variant of NumPy's rad2deg. 
tf.experimental.numpy.rad2deg(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.rad2deg.
Module: tf.experimental.numpy.random Public API for tf.experimental.numpy.random namespace. Functions rand(...): TensorFlow variant of NumPy's random.rand. randint(...): TensorFlow variant of NumPy's random.randint. randn(...): TensorFlow variant of NumPy's random.randn. random(...): TensorFlow variant of NumPy's random.random. seed(...): TensorFlow variant of NumPy's random.seed. uniform(...): TensorFlow variant of NumPy's random.uniform.
tf.experimental.numpy.random.rand TensorFlow variant of NumPy's random.rand. 
tf.experimental.numpy.random.rand(
    *size
)
 See the NumPy documentation for numpy.random.rand.
tf.experimental.numpy.random.randint TensorFlow variant of NumPy's random.randint. 
tf.experimental.numpy.random.randint(
    low, high=None, size=None, dtype=onp.int
)
 See the NumPy documentation for numpy.random.randint.
tf.experimental.numpy.random.randn TensorFlow variant of NumPy's random.randn. 
tf.experimental.numpy.random.randn(
    *args
)
 Returns samples from a normal distribution. Uses tf.random_normal. Args: *args: The shape of the output array. Returns: An ndarray with shape args and dtype float64. See the NumPy documentation for numpy.random.randn.
tf.experimental.numpy.random.random TensorFlow variant of NumPy's random.random. 
tf.experimental.numpy.random.random(
    size=None
)
 See the NumPy documentation for numpy.random.random.
tf.experimental.numpy.random.seed TensorFlow variant of NumPy's random.seed. 
tf.experimental.numpy.random.seed(
    s
)
 Sets the seed for the random number generator. Uses tf.set_random_seed. Args: s: an integer. See the NumPy documentation for numpy.random.seed.
tf.experimental.numpy.random.uniform TensorFlow variant of NumPy's random.uniform. 
tf.experimental.numpy.random.uniform(
    low=0.0, high=1.0, size=None
)
 See the NumPy documentation for numpy.random.uniform.
tf.experimental.numpy.ravel TensorFlow variant of NumPy's ravel.  View aliases  Main aliases 
tf.experimental.numpy.ndarray.ravel  
tf.experimental.numpy.ravel(
    a
)
 Unsupported arguments: order. See the NumPy documentation for numpy.ravel.
tf.experimental.numpy.real TensorFlow variant of NumPy's real. 
tf.experimental.numpy.real(
    val
)
 See the NumPy documentation for numpy.real.
tf.experimental.numpy.reciprocal TensorFlow variant of NumPy's reciprocal. 
tf.experimental.numpy.reciprocal(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.reciprocal.
tf.experimental.numpy.remainder TensorFlow variant of NumPy's remainder. 
tf.experimental.numpy.remainder(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.remainder.
tf.experimental.numpy.repeat TensorFlow variant of NumPy's repeat. 
tf.experimental.numpy.repeat(
    a, repeats, axis=None
)
 See the NumPy documentation for numpy.repeat.
tf.experimental.numpy.reshape TensorFlow variant of NumPy's reshape. 
tf.experimental.numpy.reshape(
    a, newshape, order='C'
)
 order argument can only b 'C' or 'F'. See the NumPy documentation for numpy.reshape.
tf.experimental.numpy.result_type TensorFlow variant of NumPy's result_type. 
tf.experimental.numpy.result_type(
    *arrays_and_dtypes
)
 See the NumPy documentation for numpy.result_type.
tf.experimental.numpy.roll TensorFlow variant of NumPy's roll. 
tf.experimental.numpy.roll(
    a, shift, axis=None
)
 See the NumPy documentation for numpy.roll.
tf.experimental.numpy.rot90 TensorFlow variant of NumPy's rot90. 
tf.experimental.numpy.rot90(
    m, k=1, axes=(0, 1)
)
 See the NumPy documentation for numpy.rot90.
tf.experimental.numpy.round TensorFlow variant of NumPy's round. 
tf.experimental.numpy.round(
    a, decimals=0
)
 Unsupported arguments: out. See the NumPy documentation for numpy.round.
tf.experimental.numpy.select TensorFlow variant of NumPy's select. 
tf.experimental.numpy.select(
    condlist, choicelist, default=0
)
 See the NumPy documentation for numpy.select.
tf.experimental.numpy.shape TensorFlow variant of NumPy's shape. 
tf.experimental.numpy.shape(
    a
)
 See the NumPy documentation for numpy.shape.
tf.experimental.numpy.sign TensorFlow variant of NumPy's sign. 
tf.experimental.numpy.sign(
    x, out=None, where=None, **kwargs
)
 See the NumPy documentation for numpy.sign.
tf.experimental.numpy.signbit TensorFlow variant of NumPy's signbit. 
tf.experimental.numpy.signbit(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.signbit.
tf.experimental.numpy.sin TensorFlow variant of NumPy's sin. 
tf.experimental.numpy.sin(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.sin.
tf.experimental.numpy.sinc TensorFlow variant of NumPy's sinc. 
tf.experimental.numpy.sinc(
    x
)
 See the NumPy documentation for numpy.sinc.
tf.experimental.numpy.sinh TensorFlow variant of NumPy's sinh. 
tf.experimental.numpy.sinh(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.sinh.
tf.experimental.numpy.size TensorFlow variant of NumPy's size. 
tf.experimental.numpy.size(
    x, axis=None
)
 Unsupported arguments: a. See the NumPy documentation for numpy.size.
tf.experimental.numpy.sort TensorFlow variant of NumPy's sort. 
tf.experimental.numpy.sort(
    a, axis=-1, kind='quicksort', order=None
)
 See the NumPy documentation for numpy.sort.
tf.experimental.numpy.split TensorFlow variant of NumPy's split. 
tf.experimental.numpy.split(
    ary, indices_or_sections, axis=0
)
 See the NumPy documentation for numpy.split.
tf.experimental.numpy.sqrt TensorFlow variant of NumPy's sqrt. 
tf.experimental.numpy.sqrt(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.sqrt.
tf.experimental.numpy.square TensorFlow variant of NumPy's square. 
tf.experimental.numpy.square(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.square.
tf.experimental.numpy.squeeze TensorFlow variant of NumPy's squeeze. 
tf.experimental.numpy.squeeze(
    a, axis=None
)
 See the NumPy documentation for numpy.squeeze.
tf.experimental.numpy.stack TensorFlow variant of NumPy's stack. 
tf.experimental.numpy.stack(
    arrays, axis=0
)
 Unsupported arguments: out. See the NumPy documentation for numpy.stack.
tf.experimental.numpy.std TensorFlow variant of NumPy's std. 
tf.experimental.numpy.std(
    a, axis=None, keepdims=None
)
 Unsupported arguments: dtype, out, ddof. See the NumPy documentation for numpy.std.
tf.experimental.numpy.subtract TensorFlow variant of NumPy's subtract. 
tf.experimental.numpy.subtract(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.subtract.
tf.experimental.numpy.sum TensorFlow variant of NumPy's sum. 
tf.experimental.numpy.sum(
    a, axis=None, dtype=None, keepdims=None
)
 Unsupported arguments: out, initial, where. See the NumPy documentation for numpy.sum.
tf.experimental.numpy.swapaxes TensorFlow variant of NumPy's swapaxes. 
tf.experimental.numpy.swapaxes(
    a, axis1, axis2
)
 See the NumPy documentation for numpy.swapaxes.
tf.experimental.numpy.take TensorFlow variant of NumPy's take. 
tf.experimental.numpy.take(
    a, indices, axis=None, out=None, mode='clip'
)
 out argument is not supported, and default mode is clip. See the NumPy documentation for numpy.take.
tf.experimental.numpy.take_along_axis TensorFlow variant of NumPy's take_along_axis. 
tf.experimental.numpy.take_along_axis(
    arr, indices, axis
)
 See the NumPy documentation for numpy.take_along_axis.
tf.experimental.numpy.tan TensorFlow variant of NumPy's tan. 
tf.experimental.numpy.tan(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.tan.
tf.experimental.numpy.tanh TensorFlow variant of NumPy's tanh. 
tf.experimental.numpy.tanh(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.tanh.
tf.experimental.numpy.tensordot TensorFlow variant of NumPy's tensordot. 
tf.experimental.numpy.tensordot(
    a, b, axes=2
)
 See the NumPy documentation for numpy.tensordot.
tf.experimental.numpy.tile TensorFlow variant of NumPy's tile. 
tf.experimental.numpy.tile(
    a, reps
)
 See the NumPy documentation for numpy.tile.
tf.experimental.numpy.trace TensorFlow variant of NumPy's trace. 
tf.experimental.numpy.trace(
    a, offset=0, axis1=0, axis2=1, dtype=None
)
 Unsupported arguments: out. See the NumPy documentation for numpy.trace.
tf.experimental.numpy.transpose TensorFlow variant of NumPy's transpose.  View aliases  Main aliases 
tf.experimental.numpy.ndarray.transpose  
tf.experimental.numpy.transpose(
    a, axes=None
)
 See the NumPy documentation for numpy.transpose.
tf.experimental.numpy.tri TensorFlow variant of NumPy's tri. 
tf.experimental.numpy.tri(
    N, M=None, k=0, dtype=None
)
 See the NumPy documentation for numpy.tri.
tf.experimental.numpy.tril TensorFlow variant of NumPy's tril. 
tf.experimental.numpy.tril(
    m, k=0
)
 See the NumPy documentation for numpy.tril.
tf.experimental.numpy.triu TensorFlow variant of NumPy's triu. 
tf.experimental.numpy.triu(
    m, k=0
)
 See the NumPy documentation for numpy.triu.
tf.experimental.numpy.true_divide TensorFlow variant of NumPy's true_divide. 
tf.experimental.numpy.true_divide(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.true_divide.
tf.experimental.numpy.vander TensorFlow variant of NumPy's vander. 
tf.experimental.numpy.vander(
    x, N=None, increasing=False
)
 See the NumPy documentation for numpy.vander.
tf.experimental.numpy.var TensorFlow variant of NumPy's var. 
tf.experimental.numpy.var(
    a, axis=None, dtype=None, out=None, ddof=0, keepdims=None
)
 See the NumPy documentation for numpy.var.
tf.experimental.numpy.vdot TensorFlow variant of NumPy's vdot. 
tf.experimental.numpy.vdot(
    a, b
)
 See the NumPy documentation for numpy.vdot.
tf.experimental.numpy.vsplit TensorFlow variant of NumPy's vsplit. 
tf.experimental.numpy.vsplit(
    ary, indices_or_sections
)
 See the NumPy documentation for numpy.vsplit.
tf.experimental.numpy.vstack TensorFlow variant of NumPy's vstack. 
tf.experimental.numpy.vstack(
    tup
)
 See the NumPy documentation for numpy.vstack.
tf.experimental.numpy.where TensorFlow variant of NumPy's where. 
tf.experimental.numpy.where(
    condition, x=None, y=None
)
 Raises ValueError if exactly one of x or y is not None. See the NumPy documentation for numpy.where.
tf.experimental.numpy.zeros TensorFlow variant of NumPy's zeros. 
tf.experimental.numpy.zeros(
    shape, dtype=float
)
 See the NumPy documentation for numpy.zeros.
tf.experimental.numpy.zeros_like TensorFlow variant of NumPy's zeros_like. 
tf.experimental.numpy.zeros_like(
    a, dtype=None
)
 Unsupported arguments: order, subok, shape. See the NumPy documentation for numpy.zeros_like.
tf.experimental.register_filesystem_plugin Loads a TensorFlow FileSystem plugin.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.experimental.register_filesystem_plugin  
tf.experimental.register_filesystem_plugin(
    plugin_location
)

 


 Args
  plugin_location   Path to the plugin. Relative or absolute filesystem plugin path to a dynamic library file.   
 


 Returns   None  

 


 Raises
  OSError   When the file to be loaded is not found.  
  RuntimeError   when unable to load the library.
Module: tf.experimental.tensorrt Public API for tf.experimental.tensorrt namespace. Classes class ConversionParams: Parameters that are used for TF-TRT conversion. class Converter: An offline converter for TF-TRT transformation for TF 2.0 SavedModels.
tf.experimental.tensorrt.ConversionParams Parameters that are used for TF-TRT conversion. 
tf.experimental.tensorrt.ConversionParams(
    rewriter_config_template=None,
    max_workspace_size_bytes=DEFAULT_TRT_MAX_WORKSPACE_SIZE_BYTES,
    precision_mode=TrtPrecisionMode.FP32, minimum_segment_size=3,
    is_dynamic_op=True, maximum_cached_engines=1, use_calibration=True,
    max_batch_size=1, allow_build_at_runtime=True
)
 Fields:  
rewriter_config_template: a template RewriterConfig proto used to create a TRT-enabled RewriterConfig. If None, it will use a default one. 
max_workspace_size_bytes: the maximum GPU temporary memory which the TRT engine can use at execution time. This corresponds to the 'workspaceSize' parameter of nvinfer1::IBuilder::setMaxWorkspaceSize(). 
precision_mode: one the strings in TrtPrecisionMode.supported_precision_modes(). 
minimum_segment_size: the minimum number of nodes required for a subgraph to be replaced by TRTEngineOp. 
is_dynamic_op: whether to generate dynamic TRT ops which will build the TRT network and engine at run time. i.e. Since TensorRT version < 6.0 does not support dynamic dimensions other than the batch dimension, when the TensorFlow graph has a non-batch dimension of dynamic size, we would need to enable this option. This option should be set to True in TF 2.0. 
maximum_cached_engines: max number of cached TRT engines for dynamic TRT ops. Created TRT engines for a dynamic dimension are cached. This is the maximum number of engines that can be cached. If the number of cached engines is already at max but none of them supports the input shapes, the TRTEngineOp will fall back to run the original TF subgraph that corresponds to the TRTEngineOp. 
use_calibration: this argument is ignored if precision_mode is not INT8. If set to True, a calibration graph will be created to calibrate the missing ranges. The calibration graph must be converted to an inference graph by running calibration with calibrate(). If set to False, quantization nodes will be expected for every tensor in the graph (excluding those which will be fused). If a range is missing, an error will occur. Please note that accuracy may be negatively affected if there is a mismatch between which tensors TRT quantizes and which tensors were trained with fake quantization. 
max_batch_size: max size for the input batch. This parameter is only effective when use_implicit_batch is true. 
allow_build_at_runtime: whether to build TensorRT engines during runtime. If no TensorRT engine can be found in cache that can handle the given inputs during runtime, then a new TensorRT engine is built at runtime if allow_build_at_runtime=True, and otherwise native TF is used. This argument is only effective if is_dynamic_op=True. 
 


 Attributes
  rewriter_config_template  
 
  max_workspace_size_bytes  
 
  precision_mode  
 
  minimum_segment_size  
 
  is_dynamic_op  
 
  maximum_cached_engines  
 
  use_calibration  
 
  max_batch_size  
 
  allow_build_at_runtime
tf.extract_volume_patches Extract patches from input and put them in the "depth" output dimension. 3D extension of extract_image_patches.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.extract_volume_patches  
tf.extract_volume_patches(
    input, ksizes, strides, padding, name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. 5-D Tensor with shape [batch, in_planes, in_rows, in_cols, depth].  
  ksizes   A list of ints that has length >= 5. The size of the sliding window for each dimension of input.  
  strides   A list of ints that has length >= 5. 1-D of length 5. How far the centers of two consecutive patches are in input. Must be: [1, stride_planes, stride_rows, stride_cols, 1].  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use. The size-related attributes are specified as follows: ksizes = [1, ksize_planes, ksize_rows, ksize_cols, 1]
strides = [1, stride_planes, strides_rows, strides_cols, 1]

 
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.eye     View source on GitHub    Construct an identity matrix, or a batch of matrices.  View aliases  Main aliases 
tf.linalg.eye Compat aliases for migration See Migration guide for more details. tf.compat.v1.eye, tf.compat.v1.linalg.eye  
tf.eye(
    num_rows, num_columns=None, batch_shape=None, dtype=tf.dtypes.float32, name=None
)
 See also tf.ones, tf.zeros, tf.fill, tf.one_hot. # Construct one identity matrix.
tf.eye(2)
==> [[1., 0.],
     [0., 1.]]

# Construct a batch of 3 identity matrices, each 2 x 2.
# batch_identity[i, :, :] is a 2 x 2 identity matrix, i = 0, 1, 2.
batch_identity = tf.eye(2, batch_shape=[3])

# Construct one 2 x 3 "identity" matrix
tf.eye(2, num_columns=3)
==> [[ 1.,  0.,  0.],
     [ 0.,  1.,  0.]]

 


 Args
  num_rows   Non-negative int32 scalar Tensor giving the number of rows in each batch matrix.  
  num_columns   Optional non-negative int32 scalar Tensor giving the number of columns in each batch matrix. Defaults to num_rows.  
  batch_shape   A list or tuple of Python integers or a 1-D int32 Tensor. If provided, the returned Tensor will have leading batch dimensions of this shape.  
  dtype   The type of an element in the resulting Tensor  
  name   A name for this Op. Defaults to "eye".   
 


 Returns   A Tensor of shape batch_shape + [num_rows, num_columns]
Module: tf.feature_column Public API for tf.feature_column namespace. Functions bucketized_column(...): Represents discretized dense input bucketed by boundaries. categorical_column_with_hash_bucket(...): Represents sparse feature where ids are set by hashing. categorical_column_with_identity(...): A CategoricalColumn that returns identity values. categorical_column_with_vocabulary_file(...): A CategoricalColumn with a vocabulary file. categorical_column_with_vocabulary_list(...): A CategoricalColumn with in-memory vocabulary. crossed_column(...): Returns a column for performing crosses of categorical features. embedding_column(...): DenseColumn that converts from sparse, categorical input. indicator_column(...): Represents multi-hot representation of given categorical column. make_parse_example_spec(...): Creates parsing spec dictionary from input feature_columns. numeric_column(...): Represents real valued or numerical features. sequence_categorical_column_with_hash_bucket(...): A sequence of categorical terms where ids are set by hashing. sequence_categorical_column_with_identity(...): Returns a feature column that represents sequences of integers. sequence_categorical_column_with_vocabulary_file(...): A sequence of categorical terms where ids use a vocabulary file. sequence_categorical_column_with_vocabulary_list(...): A sequence of categorical terms where ids use an in-memory list. sequence_numeric_column(...): Returns a feature column that represents sequences of numeric data. shared_embeddings(...): List of dense columns that convert from sparse, categorical input. weighted_categorical_column(...): Applies weight values to a CategoricalColumn.