I am able to fix the error it with the following code:
issue was the wrong way to concatenate the array list:

def __init__(self,  path, use_tokenizer, max_sequence_len=None):
    # Check max sequence length.
    df = pd.read_csv(path,sep=',')
    max_sequence_len = use_tokenizer.max_len if max_sequence_len is None else max_sequence_len
    texts = []
    labels = []
    # Since the labels are defined by folders with data we loop 
    # through each label.
    #Index(['source', 'label', 'pubDate', 'title', 'author', 'content', 'pub'], dtype='object')
    
    for index, row in df.iterrows():
        source = row['source']
        pubDate = row['pubDate']
        author = row['author']
        title = row['title']
        content = row['content']

        content = fix_text(content)
        texts.append(source)
        texts.append(author)
        texts.append(title)
        texts.append(content)

        label_id=row['label']
        # Save encode labels.
        labels.append(label_id)


    # Number of exmaples.
    self.n_examples = len(labels)
  
    # Use tokenizer on texts. This can take a while.
    print('Using tokenizer on all texts. This can take a while...')
    self.inputs = use_tokenizer(texts, add_special_tokens=True, truncation=True, padding=True, return_tensors='pt',  max_length=max_sequence_len)
    # Get maximum sequence length.
    self.sequence_len = self.inputs['input_ids'].shape[-1]
    print('Texts padded or truncated to %d length!' % self.sequence_len)
    # Add labels.
    self.inputs.update({'labels':torch.tensor(labels)})
    print('Finished!\n')

    return
#