curl --request POST \
  --url https://api.tensormesh.ai/v1/support/reserved-deployments \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "fullName": "<string>",
  "workEmail": "<string>",
  "company": "<string>",
  "jobTitle": "<string>",
  "preferredGpu": "GPU_TYPE_UNSPECIFIED",
  "totalGpusNeeded": "<string>",
  "deploymentTimeline": "<string>",
  "workloadUseCase": "<string>",
  "networkingStorage": "<string>",
  "otherNotes": "<string>"
}
'

import requests

url = "https://api.tensormesh.ai/v1/support/reserved-deployments"

payload = {
    "fullName": "<string>",
    "workEmail": "<string>",
    "company": "<string>",
    "jobTitle": "<string>",
    "preferredGpu": "GPU_TYPE_UNSPECIFIED",
    "totalGpusNeeded": "<string>",
    "deploymentTimeline": "<string>",
    "workloadUseCase": "<string>",
    "networkingStorage": "<string>",
    "otherNotes": "<string>"
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    fullName: '<string>',
    workEmail: '<string>',
    company: '<string>',
    jobTitle: '<string>',
    preferredGpu: 'GPU_TYPE_UNSPECIFIED',
    totalGpusNeeded: '<string>',
    deploymentTimeline: '<string>',
    workloadUseCase: '<string>',
    networkingStorage: '<string>',
    otherNotes: '<string>'
  })
};

fetch('https://api.tensormesh.ai/v1/support/reserved-deployments', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

{
  "requestId": "<string>"
}

{
  "code": 123,
  "message": "<string>",
  "details": [
    {
      "@type": "<string>"
    }
  ]
}