Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
 in the Software without restriction, including without limitation the rights
 to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 copies of the Software, and to permit persons to whom the Software is
 furnished to do so, subject to the following conditions:

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

# Hermetic Python

Hermetic Python allows not to rely on system-installed Python, and
system-installed Python packages. \
Instead, an independent Python toolchain is registered, ensuring the right
dependencies are always used. \
See https://github.com/bazelbuild/rules_python/ for more details.

### Specifying the Python version

Note: Only a number of minor Python versions are supported at any given time.

ASM: a very small and fast Java bytecode manipulation framework
Copyright (c) 2000-2011 INRIA, France Telecom
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
1. Redistributions of source code must retain the above copyright
  notice, this list of conditions and the following disclaimer.

Copyright (c) 2014 The Go-FlowRate Authors. All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:

 * Redistributions of source code must retain the above copyright
   notice, this list of conditions and the following disclaimer.

 * Redistributions in binary form must reproduce the above copyright

= vendor/github.com/antlr4-go/antlr/v4 licensed under: =

Copyright (c) 2012-2023 The ANTLR Project. All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:

1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.

# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# 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,

   0340; COMBINING GRAVE TONE MARK
   0341; COMBINING ACUTE TONE MARK
   200E; LEFT-TO-RIGHT MARK
   200F; RIGHT-TO-LEFT MARK
   202A; LEFT-TO-RIGHT EMBEDDING
   202B; RIGHT-TO-LEFT EMBEDDING
   202C; POP DIRECTIONAL FORMATTING
   202D; LEFT-TO-RIGHT OVERRIDE
   202E; RIGHT-TO-LEFT OVERRIDE
   206A; INHIBIT SYMMETRIC SWAPPING
   206B; ACTIVATE SYMMETRIC SWAPPING
   206C; INHIBIT ARABIC FORM SHAPING
   206D; ACTIVATE ARABIC FORM SHAPING

*A noticeable trait of this expansion is that it chooses unique hosts such the setup provides maximum protection and availability.*

- Choosing an erasure set for the object is decided during `PutObject()`, object names are used to find the right erasure set using the following pseudo code.

```go
// hashes the key returning an integer.
func sipHashMod(key string, cardinality int, id [16]byte) int {
        if cardinality <= 0 {
                return -1

    *   `steps_per_epoch` and `steps` arguments are supported with numpy arrays.
    *   New error message when unexpected keys are used in
        sample_weight/class_weight dictionaries
    *   Losses are scaled in Keras compile/fit and not in the optimizers
        anymore. If you are using custom training loop, we have new utilities to