code: def set_diag(
      input, diagonal, /, *, k=0, align="RIGHT_LEFT", name=None
  ):
      # TODO:
      #  1. Add support for different k values and align options
      #  2. Add support for input tensors with ranks larger than 3
  
      # Convert input and diagonal to Ivy array format
      input, diagonal = map(ivy.array, (input, diagonal))
  
      # Check if the input tensor has a rank larger than 3
      if input.ndim > 3:
          raise ivy.utils.exceptions.IvyNotImplementedException(
              "Input tensor must have rank less than or equal to 3.\nInput shape:"
              f" {input.shape}"
          )
  
      # Check if the first dimension of the input and diagonal match
      if input.shape[0] != diagonal.shape[0]:
          raise ivy.utils.exceptions.IvyValueError(
              "Number of diagonal vectors must match the number of matrices in the"
              f" input.\nInput shape: {input.shape}, Diagonal shape: {diagonal.shape}"
          )
  
      # Handle the case where input is a 2D matrix
      if input.ndim < 3:
          # Check the diagonal length matches the first dimension of the matrix
          if input.shape[0] != diagonal.shape[0]:
              raise ivy.utils.exceptions.IvyValueError(
                  "Length of the diagonal vector must match the first dimension of the"
                  f" matrix.\nMatrix shape: {input.shape}, Diagonal shape:"
                  f" {diagonal.shape}"
              )
  
          input[range(input.shape[0]), range(input.shape[0])] = diagonal
      else:
          for matrix, new_diagonal in zip(input, diagonal):
              # Check the diagonal length matches the first dimension of the matrix
              if matrix.shape[0] != new_diagonal.shape[0]:
                  raise ivy.utils.exceptions.IvyValueError(
                      "Length of the diagonal vector must match the first dimension of"
                      f" the matrix.\nMatrix shape: {matrix.shape}, Diagonal shape:"
                      f" {new_diagonal.shape}"
                  )
  
              matrix[
                  range(matrix.shape[0]), range(matrix.shape[0])
              ] = new_diagonal
  
      return input
  
imports: import ivy
  from ivy.functional.frontends.tensorflow import check_tensorflow_casting
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from ivy.functional.frontends.tensorflow.func_wrapper import to_ivy_arrays_and_back, handle_tf_dtype
  import ivy.functional.frontends.tensorflow
label_desc: Use image node_id 27 for calling a global function with example usage: set_diag() and returns: input