Function convertToTensor

tfjs-core/src/tensor_util_env.ts:98–131

(
    x: T|TensorLike, argName: string, functionName: string,
    parseAsDtype: DataType|'numeric'|'string_or_numeric' = 'numeric')

}

export function convertToTensor<T extends Tensor>(
    x: T|TensorLike, argName: string, functionName: string,
    parseAsDtype: DataType|'numeric'|'string_or_numeric' = 'numeric'): T {
  if (x instanceof getGlobalTensorClass()) {
    assertDtype(parseAsDtype, x.dtype, argName, functionName);
    return x;
  }
  let inferredDtype = inferDtype(x);
  // If the user expects a bool/int/float, use that info to update the
  // inferredDtype when it is not a string.
  if (inferredDtype !== 'string' &&
      ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {
    inferredDtype = parseAsDtype as DataType;
  }
  assertDtype(parseAsDtype, inferredDtype, argName, functionName);

  if ((x == null) ||
      (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&
       typeof x !== 'boolean' && typeof x !== 'string')) {
    const type = x == null ? 'null' : (x as {}).constructor.name;
    throw new Error(
        `Argument '${argName}' passed to '${functionName}' must be a ` +
        `Tensor or TensorLike, but got '${type}'`);
  }
  const inferredShape = inferShape(x, inferredDtype);
  if (!isTypedArray(x) && !Array.isArray(x)) {
    x = [x] as number[];
  }
  const skipTypedArray = true;
  const values = inferredDtype !== 'string' ?
      toTypedArray(x, inferredDtype as DataType) :
      flatten(x as string[], [], skipTypedArray) as string[];
  return ENGINE.makeTensor(values, inferredShape, inferredDtype) as T;
}

export function convertToTensorArray<T extends Tensor>(
    arg: Array<T|TensorLike>, argName: string, functionName: string,