EVOLUTION-MANAGER

Edit File: __init__.py

# This file is MACHINE GENERATED! Do not edit. # Generated by: tensorflow/python/tools/api/generator/create_python_api.py script. """Ragged Tensors. This package defines ops for manipulating ragged tensors (`tf.RaggedTensor`), which are tensors with non-uniform shapes. In particular, each `RaggedTensor` has one or more *ragged dimensions*, which are dimensions whose slices may have different lengths. For example, the inner (column) dimension of `rt=[[3, 1, 4, 1], [], [5, 9, 2], [6], []]` is ragged, since the column slices (`rt[0, :]`, ..., `rt[4, :]`) have different lengths. For a more detailed description of ragged tensors, see the `tf.RaggedTensor` class documentation and the [Ragged Tensor Guide](/guide/ragged_tensors). ### Additional ops that support `RaggedTensor` Arguments that accept `RaggedTensor`s are marked in **bold**. * `tf.batch_gather`(**params**, **indices**, name=`None`) * `tf.bitwise.bitwise_and`(**x**, **y**, name=`None`) * `tf.bitwise.bitwise_or`(**x**, **y**, name=`None`) * `tf.bitwise.bitwise_xor`(**x**, **y**, name=`None`) * `tf.bitwise.invert`(**x**, name=`None`) * `tf.bitwise.left_shift`(**x**, **y**, name=`None`) * `tf.bitwise.right_shift`(**x**, **y**, name=`None`) * `tf.cast`(**x**, dtype, name=`None`) * `tf.clip_by_value`(**t**, clip_value_min, clip_value_max, name=`None`) * `tf.concat`(**values**, axis, name=`'concat'`) * `tf.debugging.check_numerics`(**tensor**, message, name=`None`) * `tf.dtypes.complex`(**real**, **imag**, name=`None`) * `tf.dtypes.saturate_cast`(**value**, dtype, name=`None`) * `tf.dynamic_partition`(**data**, **partitions**, num_partitions, name=`None`) * `tf.expand_dims`(**input**, axis=`None`, name=`None`, dim=`None`) * `tf.gather_nd`(**params**, **indices**, name=`None`, batch_dims=`0`) * `tf.gather`(**params**, **indices**, validate_indices=`None`, name=`None`, axis=`None`, batch_dims=`0`) * `tf.identity`(**input**, name=`None`) * `tf.io.decode_base64`(**input**, name=`None`) * `tf.io.decode_compressed`(**bytes**, compression_type=`''`, name=`None`) * `tf.io.encode_base64`(**input**, pad=`False`, name=`None`) * `tf.math.abs`(**x**, name=`None`) * `tf.math.acos`(**x**, name=`None`) * `tf.math.acosh`(**x**, name=`None`) * `tf.math.add_n`(**inputs**, name=`None`) * `tf.math.add`(**x**, **y**, name=`None`) * `tf.math.angle`(**input**, name=`None`) * `tf.math.asin`(**x**, name=`None`) * `tf.math.asinh`(**x**, name=`None`) * `tf.math.atan2`(**y**, **x**, name=`None`) * `tf.math.atan`(**x**, name=`None`) * `tf.math.atanh`(**x**, name=`None`) * `tf.math.ceil`(**x**, name=`None`) * `tf.math.conj`(**x**, name=`None`) * `tf.math.cos`(**x**, name=`None`) * `tf.math.cosh`(**x**, name=`None`) * `tf.math.digamma`(**x**, name=`None`) * `tf.math.divide_no_nan`(**x**, **y**, name=`None`) * `tf.math.divide`(**x**, **y**, name=`None`) * `tf.math.equal`(**x**, **y**, name=`None`) * `tf.math.erf`(**x**, name=`None`) * `tf.math.erfc`(**x**, name=`None`) * `tf.math.erfinv`(**x**, name=`None`) * `tf.math.exp`(**x**, name=`None`) * `tf.math.expm1`(**x**, name=`None`) * `tf.math.floor`(**x**, name=`None`) * `tf.math.floordiv`(**x**, **y**, name=`None`) * `tf.math.floormod`(**x**, **y**, name=`None`) * `tf.math.greater_equal`(**x**, **y**, name=`None`) * `tf.math.greater`(**x**, **y**, name=`None`) * `tf.math.imag`(**input**, name=`None`) * `tf.math.is_finite`(**x**, name=`None`) * `tf.math.is_inf`(**x**, name=`None`) * `tf.math.is_nan`(**x**, name=`None`) * `tf.math.less_equal`(**x**, **y**, name=`None`) * `tf.math.less`(**x**, **y**, name=`None`) * `tf.math.lgamma`(**x**, name=`None`) * `tf.math.log1p`(**x**, name=`None`) * `tf.math.log_sigmoid`(**x**, name=`None`) * `tf.math.log`(**x**, name=`None`) * `tf.math.logical_and`(**x**, **y**, name=`None`) * `tf.math.logical_not`(**x**, name=`None`) * `tf.math.logical_or`(**x**, **y**, name=`None`) * `tf.math.logical_xor`(**x**, **y**, name=`'LogicalXor'`) * `tf.math.maximum`(**x**, **y**, name=`None`) * `tf.math.minimum`(**x**, **y**, name=`None`) * `tf.math.multiply`(**x**, **y**, name=`None`) * `tf.math.ndtri`(**x**, name=`None`) * `tf.math.negative`(**x**, name=`None`) * `tf.math.not_equal`(**x**, **y**, name=`None`) * `tf.math.pow`(**x**, **y**, name=`None`) * `tf.math.real`(**input**, name=`None`) * `tf.math.reciprocal`(**x**, name=`None`) * `tf.math.reduce_all`(**input_tensor**, axis=`None`, keepdims=`False`, name=`None`) * `tf.math.reduce_any`(**input_tensor**, axis=`None`, keepdims=`False`, name=`None`) * `tf.math.reduce_max`(**input_tensor**, axis=`None`, keepdims=`False`, name=`None`) * `tf.math.reduce_mean`(**input_tensor**, axis=`None`, keepdims=`False`, name=`None`) * `tf.math.reduce_min`(**input_tensor**, axis=`None`, keepdims=`False`, name=`None`) * `tf.math.reduce_prod`(**input_tensor**, axis=`None`, keepdims=`False`, name=`None`) * `tf.math.reduce_sum`(**input_tensor**, axis=`None`, keepdims=`False`, name=`None`) * `tf.math.rint`(**x**, name=`None`) * `tf.math.round`(**x**, name=`None`) * `tf.math.rsqrt`(**x**, name=`None`) * `tf.math.sign`(**x**, name=`None`) * `tf.math.sin`(**x**, name=`None`) * `tf.math.sinh`(**x**, name=`None`) * `tf.math.sqrt`(**x**, name=`None`) * `tf.math.square`(**x**, name=`None`) * `tf.math.squared_difference`(**x**, **y**, name=`None`) * `tf.math.subtract`(**x**, **y**, name=`None`) * `tf.math.tan`(**x**, name=`None`) * `tf.math.truediv`(**x**, **y**, name=`None`) * `tf.math.unsorted_segment_max`(**data**, **segment_ids**, num_segments, name=`None`) * `tf.math.unsorted_segment_mean`(**data**, **segment_ids**, num_segments, name=`None`) * `tf.math.unsorted_segment_min`(**data**, **segment_ids**, num_segments, name=`None`) * `tf.math.unsorted_segment_prod`(**data**, **segment_ids**, num_segments, name=`None`) * `tf.math.unsorted_segment_sqrt_n`(**data**, **segment_ids**, num_segments, name=`None`) * `tf.math.unsorted_segment_sum`(**data**, **segment_ids**, num_segments, name=`None`) * `tf.nn.dropout`(**x**, keep_prob=`None`, noise_shape=`None`, seed=`None`, name=`None`, rate=`None`) * `tf.one_hot`(**indices**, depth, on_value=`None`, off_value=`None`, axis=`None`, dtype=`None`, name=`None`) * `tf.ones_like`(**tensor**, dtype=`None`, name=`None`, optimize=`True`) * `tf.print`(***inputs**, **kwargs) * `tf.rank`(**input**, name=`None`) * `tf.realdiv`(**x**, **y**, name=`None`) * `tf.reverse`(**tensor**, axis, name=`None`) * `tf.size`(**input**, name=`None`, out_type=`tf.int32`) * `tf.squeeze`(**input**, axis=`None`, name=`None`, squeeze_dims=`None`) * `tf.stack`(**values**, axis=`0`, name=`'stack'`) * `tf.strings.as_string`(**input**, precision=`-1`, scientific=`False`, shortest=`False`, width=`-1`, fill=`''`, name=`None`) * `tf.strings.format`(template, **inputs**, placeholder=`'{}'`, summarize=`3`, name=`None`) * `tf.strings.join`(**inputs**, separator=`''`, name=`None`) * `tf.strings.length`(**input**, name=`None`, unit=`'BYTE'`) * `tf.strings.reduce_join`(**inputs**, axis=`None`, keepdims=`False`, separator=`''`, name=`None`) * `tf.strings.regex_full_match`(**input**, pattern, name=`None`) * `tf.strings.regex_replace`(**input**, pattern, rewrite, replace_global=`True`, name=`None`) * `tf.strings.strip`(**input**, name=`None`) * `tf.strings.substr`(**input**, pos, len, name=`None`, unit=`'BYTE'`) * `tf.strings.to_hash_bucket_fast`(**input**, num_buckets, name=`None`) * `tf.strings.to_hash_bucket_strong`(**input**, num_buckets, key, name=`None`) * `tf.strings.to_hash_bucket`(**input**, num_buckets, name=`None`) * `tf.strings.to_hash_bucket`(**input**, num_buckets, name=`None`) * `tf.strings.to_number`(**input**, out_type=`tf.float32`, name=`None`) * `tf.strings.unicode_script`(**input**, name=`None`) * `tf.tile`(**input**, multiples, name=`None`) * `tf.truncatediv`(**x**, **y**, name=`None`) * `tf.truncatemod`(**x**, **y**, name=`None`) * `tf.where`(**condition**, **x**=`None`, **y**=`None`, name=`None`) * `tf.where`(**condition**, **x**=`None`, **y**=`None`, name=`None`) * `tf.zeros_like`(**tensor**, dtype=`None`, name=`None`, optimize=`True`)n """ from __future__ import print_function as _print_function import sys as _sys from tensorflow.python.ops.ragged.ragged_array_ops import boolean_mask from tensorflow.python.ops.ragged.ragged_array_ops import cross from tensorflow.python.ops.ragged.ragged_array_ops import cross_hashed from tensorflow.python.ops.ragged.ragged_array_ops import stack_dynamic_partitions from tensorflow.python.ops.ragged.ragged_concat_ops import stack from tensorflow.python.ops.ragged.ragged_factory_ops import constant from tensorflow.python.ops.ragged.ragged_functional_ops import map_flat_values from tensorflow.python.ops.ragged.ragged_math_ops import range from tensorflow.python.ops.ragged.segment_id_ops import row_splits_to_segment_ids from tensorflow.python.ops.ragged.segment_id_ops import segment_ids_to_row_splits del _print_function