Cannot create a ner pipeline in my training model

Question

code

with open('D:/NLP-CV Parser/1BM14TE002_ABISHEK - Abishek Yogesh_text.txt', 'rb') as f:
    data =  f.read()

# Save as pickle
with open('D:/NLP-CV Parser/1BM14TE002_ABISHEK - Abishek Yogesh_text.pkl', 'wb') as f:
    pickle.dump(data, f)

train_data = pickle.load(open('D:/NLP-CV Parser/1BM14TE002_ABISHEK - Abishek Yogesh_text.pkl', 'rb'))
train_data

nlp = spacy.blank('en')

def train_model(train_data):
    # Remove all pipelines and add NER pipeline from the model
    if 'ner'not in nlp.pipe_names:
        ner = nlp.create_pipe('ner')
        # adding NER pipeline to nlp model
        nlp.add_pipe(ner,last=True)
    
    #Add labels in the NLP pipeline
    '''for _, annotation in train_data:
        for ent in annotation['entities']:
            ner.add_label(ent[2])'''
    
    other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
    with nlp.disable_pipes(*other_pipes):  # only train NER
        optimizer = nlp.begin_training()
        for itn in range(10): # train for 10 iterations
            print("Starting iteration " + str(itn))
            random.shuffle(train_data)
            losses = {}
            index = 0
            for text, annotations in train_data:
                try:
                    nlp.update(
                        [text],  # batch of texts
                        [annotations],  # batch of annotations
                        drop=0.2,  # dropout - make it harder to memorise data
                        sgd=optimizer,  # callable to update weights
                        losses=losses)
                except Exception as e:
                    pass
                
            print(losses)
        
train_model(train_data)

I get

ValueError: [E966] nlp.add_pipe now takes the string name of the registered component factory, not a callable component. Expected string, but got <spacy.pipeline.ner.EntityRecognizer object at 0x0000022759E567A0> (name: 'None').

• If you created your component with nlp.create_pipe('name'): remove nlp.create_pipe and call nlp.add_pipe('name') instead.

• If you passed in a component like TextCategorizer(): call nlp.add_pipe with the string name instead, e.g. nlp.add_pipe('textcat').

• If you're using a custom component: Add the decorator @Language.component (for function components) or @Language.factory (for class components / factories) to your custom component and assign it a name, e.g. @Language.component('your_name'). You can then run nlp.add_pipe('your_name') to add it to the pipeline.

How to resolve this error?