/*
 * Copyright (C) 2016 The Guava 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,

/*
 * Copyright (C) 2012 The Guava 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,

/*
 * Copyright 2012-2025 CodeLibs Project and the Others.
 *
 * 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,

#  The ASF licenses this file to You 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,

			if orderBy.Columns[i].Reorder {
				orderBy.Columns = orderBy.Columns[i:]
				clause.Expression = orderBy
				return
			}
		}

		copiedColumns := make([]OrderByColumn, len(v.Columns))
		copy(copiedColumns, v.Columns)
		orderBy.Columns = append(copiedColumns, orderBy.Columns...)
	}

	clause.Expression = orderBy

# 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,

- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installed from (source or binary):
- TensorFlow version (or github SHA if from source):


**Provide the text output from tflite_convert**

```
# Copy and paste here
```

**Standalone code to reproduce the issue** 
Provide a reproducible test case that is the bare minimum necessary to generate
the problem. If possible, please share a link to Colab/Jupyter/any notebook.

// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
// GNU Affero General Public License for more details.
//
// You should have received a copy of the GNU Affero General Public License
// along with this program.  If not, see <http://www.gnu.org/licenses/>.

package cmd

import (
	"bytes"
	"crypto/rand"
	"io"
	"testing"
)

/// info | Інформація

У Pydantic v1 метод називався `.copy()`, він був застарілий (але все ще підтримується) у Pydantic v2, і був перейменований у `.model_copy()`.

Приклади тут використовують `.copy()` для сумісності з Pydantic v1, але якщо Ви можете використовувати Pydantic v2 — Вам слід використовувати `.model_copy()` замість цього.

///

// Read implements the [io.Reader] interface.
func (r *Reader) Read(b []byte) (n int, err error) {
	if r.i >= int64(len(r.s)) {
		return 0, io.EOF
	}
	r.prevRune = -1
	n = copy(b, r.s[r.i:])
	r.i += int64(n)
	return
}

// ReadAt implements the [io.ReaderAt] interface.
func (r *Reader) ReadAt(b []byte, off int64) (n int, err error) {
	// cannot modify state - see io.ReaderAt
	if off < 0 {