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logic.py
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logic.py
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# coding=utf-8
# Copyright 2019 The Trax Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Logical functions."""
import tensorflow.compat.v2 as tf
from trax.tf_numpy.numpy import array_creation
from trax.tf_numpy.numpy import arrays
from trax.tf_numpy.numpy import utils
# Relational operators.
def equal(x1, x2):
"""Compare two arrays for equality element-wise.
Both arrays must either be of the same shape or one should be broadcastable
to the other.
Args:
x1: array_like. Could be an ndarray, a Tensor or any object that can
be converted to a Tensor using `tf.convert_to_tensor`.
x2: array_like. Could be an ndarray, a Tensor or any object that can
be converted to a Tensor using `tf.convert_to_tensor`.
Returns:
An ndarray of type bool and broadcasted shape of x1 and x2.
"""
dtype = utils.result_type(x1, x2)
# Cast x1 and x2 to the result_type if needed.
x1 = array_creation.array(x1, copy=False, dtype=dtype)
x2 = array_creation.array(x2, copy=False, dtype=dtype)
return utils.tensor_to_ndarray(tf.equal(x1.data, x2.data))
setattr(arrays.ndarray, '__eq__', equal)